diff --git a/.gitattributes b/.gitattributes
index ddb69d4e2c5231c1bcd2a662e3266ef1c7e22852..a8cad345af0c3a01cd1c87fa235971d741c7afc9 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -19,3 +19,5 @@ models/intra/graph_output_4_8_float.sadl filter=lfs diff=lfs merge=lfs -text
 models/intra/graph_output_8_16_int16.sadl filter=lfs diff=lfs merge=lfs -text
 models/intra/graph_output_8_8_int16.sadl filter=lfs diff=lfs merge=lfs -text
 models/intra/graph_output_16_16_float.sadl filter=lfs diff=lfs merge=lfs -text
+models/NnlfSet0_model_float.sadl filter=lfs diff=lfs merge=lfs -text
+models/NnlfSet0_model_int16.sadl filter=lfs diff=lfs merge=lfs -text
diff --git a/models/NnlfSet0_model_float.sadl b/models/NnlfSet0_model_float.sadl
index 66a3d80a5bce9ce99f937a82812cdb1bdea48f8e..31507f41ae2b6ce4a51dae0df8df8f72aae2a094 100644
Binary files a/models/NnlfSet0_model_float.sadl and b/models/NnlfSet0_model_float.sadl differ
diff --git a/models/NnlfSet0_model_int16.sadl b/models/NnlfSet0_model_int16.sadl
index 3f827ff420fbc4ece9349dd8ae5a80e7fec6802a..1ddf4626686b75e0fcfddc6979f9cc04c2bc6ac0 100644
Binary files a/models/NnlfSet0_model_int16.sadl and b/models/NnlfSet0_model_int16.sadl differ
diff --git a/training/data_loader/data_loader.py b/training/data_loader/data_loader.py
index deebdcb4ec31371b930385d8ceb533dcf37b7544..e4b217f2d32a45012994ae506d9e79b3b7bef591 100644
--- a/training/data_loader/data_loader.py
+++ b/training/data_loader/data_loader.py
@@ -106,6 +106,7 @@ class DataLoader:
             self.normalizer_org8bits = 255.0
             self.normalizer_org10bits = 1023.0
             self.normalizer_qp   = 1023.0
+            self.normalizer_slice_type = 1023.0
         else:
             self.normalizer_rec  = 1024.0
             self.normalizer_pred = 1024.0
@@ -181,11 +182,13 @@ class DataLoader:
                  nb_w=int(w//psize - 2)
                  nb_h=int(h//psize - 2)
                  
-                 id_ra = '_T2RA_'
-                 ra_flag = True if id_ra in d['bsname'] else False
-                 for fidx in range(int(d['data_count'])):
-                    if ra_flag and (fidx == 0 or fidx == 32 or fidx == 64):
-                        continue
+                 frames = range(int(d['data_count']))
+                 if '_T2RA_' in d['bsname']:
+                    if int(d['original_intra_period']) == 32:
+                        frames = [2, 8, 16, 21, 28]
+                    else:
+                        frames = [2, 8, 16, 32, 51, 60]
+                 for fidx in frames:
                     for y0 in range(nb_h):
                         for x0 in range(nb_w):
                             self.patch_info.append(PatchInfo(didx,fidx,1+x0,1+y0))
@@ -296,6 +299,9 @@ class DataLoader:
                 norm=1
                 off=pinfo.frame_index*4
                 v = readOne(psize,bsize,norm,fn,off,'int32')
+                v[v!=2] = self.normalizer_slice_type
+                v[v==2] = 0
+                v/=self.normalizer_slice_type
             else:
                  sys.exit("Unkwown component {}".format(c))
             t[0,:,:,idx]=v
diff --git a/training/tools/concatenate_dataset.py b/training/tools/concatenate_dataset.py
index 60b3cdbb6a5241e5042c66f9c6068ef053be7320..bb5bdabd3c5c8f83004da35252c5eadfc71e6661 100644
--- a/training/tools/concatenate_dataset.py
+++ b/training/tools/concatenate_dataset.py
@@ -61,12 +61,16 @@ if args.input_dir_json is not None:
     flat=[d for d1 in args.input_dir_json for d in d1]
     for d in flat:
         files = sorted(glob.glob(d+'/*.json'))
-        if args.dataset_range:
-            temp = args.dataset_range.split('-')
-            range_start, range_end = list(map(lambda x: int(x), temp))
-            files = files[range_start:range_end]
         print("Processing directory {}: {} files".format(d,len(files)))
         for f in files:
+           if args.dataset_range:
+               temp = args.dataset_range.split('-')
+               range_start, range_end = list(map(lambda x: int(x), temp)) 
+               file_name = f.split('/')[-1]
+               temp = file_name.split('_S')[1]
+               file_idx =int(temp.split('_R')[0])
+               if file_idx < range_start or file_idx > range_end:
+                    continue
            with open(f, "r") as file:
                content = file.read()
                dcontent = json.loads(content)
@@ -116,6 +120,9 @@ if args.input_json is not None:
                   m = re.match("^FrameSkip\s*:\s*([0-9]+)", line)
                   if m:
                       info['FrameSkip']=m.group(1)
+                  m = re.match("^Intra period\s*:\s*([0-9]+)", line)
+                  if m:
+                      info['IntraPeriod']=m.group(1)                  
                   m = re.match("^Input\s+bit\s+depth\s*:\s*\(Y:([0-9]+),", line)
                   if m:
                       info['InputBitDepth']=m.group(1)
@@ -123,7 +130,7 @@ if args.input_json is not None:
                   if m:
                        info['InputBitDepth']=m.group(1)
               if name is not None:
-                  if len(info) != 4:
+                  if len(info) != 5:
                     sys.exit("Not enough information extracted for bitstream {}".format(name))
                   db_logs[name]=info        
     print(db_logs)
@@ -136,6 +143,7 @@ if args.input_json is not None:
               d['original_yuv']=info['InputFile']
               d['original_temporal_subsample']=int(info['TemporalSubsampleRatio'])
               d['original_frame_skip']=int(info['FrameSkip'])
+              d['original_intra_period']=int(info['IntraPeriod'])
 #              d['qp_base']=int(info['QP'])
               d['original_bitdepth']=int(info['InputBitDepth'])
       s = json.dumps(dcontent,indent=1)
diff --git a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/1_ReadMe.md b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/1_ReadMe.md
index 27755c2df772b5bcf4440469a244d923a6615e1c..52183ed592cb3065780a9447026f5dd31a60b46f 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/1_ReadMe.md
+++ b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/1_ReadMe.md
@@ -6,13 +6,13 @@ Generation:
 3. Obtain the raw data in **DIV2K_train_valid_HR_YUV**, which is used for the subsequent compression and also as the ground truth
 
 ## [TVD](https://multimedia.tencent.com/resources/tvd)
-All the sequences with 10 bit-depth (74 sequences) are used. (Partial dataset)
+The first 20 sequences with 10 bit-depth are used. (Partial dataset)
 Generation:
 1. Run tvd_to_yuv.py
 2. Obtain the raw data in **TVD_Video_YUV** to be used for the subsequent compression and as the ground truth 
 
 ## [BVI-DVC](https://vilab.blogs.bristol.ac.uk/2020/02/2375/)
-All the 1920x1088 sequences (200 sequences) are used. (Partial dataset)
+All the 1920x1088 sequences, 960x544 sequences and 480x272 (600 sequences) are used. (Partial dataset)
 Generation:
 1. Run bvi_dvc_to_yuv.py
 2. Obtain the raw data in **bvi_dvc_YUV** to be used for the subsequent compression and as the ground truth
diff --git a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/bvi_dvc_to_yuv.py b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/bvi_dvc_to_yuv.py
index 8d9e10ac80e070861e79925f33fa0f08c87e0a53..c7d3d56a081cfc790cf15917fa53e313c10b867e 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/bvi_dvc_to_yuv.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/bvi_dvc_to_yuv.py
@@ -31,7 +31,6 @@
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 * THE POSSIBILITY OF SUCH DAMAGE.
 """
-
 import os
 
 mp4_dir = './Videos'
@@ -40,15 +39,11 @@ if not os.path.exists(yuv_dir):
     os.makedirs(yuv_dir)
 
 for mp4_file in sorted(os.listdir(mp4_dir)):
-    if mp4_file[0] != 'B':
+    if mp4_file[0] == 'A':
         continue
     
     mp4_path = os.path.join(mp4_dir, mp4_file)
     yuv_path = os.path.join(yuv_dir, mp4_file[:-4]+'.yuv')
-    if not os.path.exists(tar):
-        os.makedirs(tar)
-    
-    size = mp4_file.split('_')[1]
     
     cmd = f'ffmpeg -i {mp4_path} -pix_fmt yuv420p10le {yuv_path}'
     os.system(cmd)
diff --git a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/div2k_to_yuv.py b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/div2k_to_yuv.py
index 236bdff0b48647c71b1e77cb996ef1a440ade327..f7e5db0a1651251e26cc74a05f9fa697571a1a22 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/div2k_to_yuv.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/div2k_to_yuv.py
@@ -31,7 +31,6 @@
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 * THE POSSIBILITY OF SUCH DAMAGE.
 """
-
 import os
 from PIL import Image
 
diff --git a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/tvd_to_yuv.py b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/tvd_to_yuv.py
index 3300dc9e5f3b95f03a4009aba838f4b34fd3b118..58af138bd73c51716214db651d1de22fc8a86aa6 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/tvd_to_yuv.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/1_generate_raw_data/tvd_to_yuv.py
@@ -31,7 +31,6 @@
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 * THE POSSIBILITY OF SUCH DAMAGE.
 """
-
 import os
 
 mp4_dir = './Video'
@@ -39,18 +38,19 @@ yuv_dir = './TVD_Video_YUV'
 if not os.path.exists(yuv_dir):
     os.makedirs(yuv_dir)
 
+cnt = 0 
 for mp4_file in sorted(os.listdir(mp4_dir)):
     if '_10bit_' not in mp4_file:
         continue
     
+    if cnt == 20:
+        break
+    
     mp4_path = os.path.join(mp4_dir, mp4_file)
     yuv_path = os.path.join(yuv_dir, mp4_file[:-4]+'.yuv')
-    if not os.path.exists(tar):
-        os.makedirs(tar)
-    
-    size = mp4_file.split('_')[1]
     
     cmd = f'ffmpeg -i {mp4_path} -pix_fmt yuv420p10le {yuv_path}'
     os.system(cmd)
+    cnt += 1
     
     
diff --git a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/2_ReadMe.md b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/2_ReadMe.md
index 394d34b78ef0d97644f648353e658c8b63847a8c..047d372de13809c682dd5f7beef558176fac667d 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/2_ReadMe.md
+++ b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/2_ReadMe.md
@@ -3,10 +3,8 @@ For the convenience, the detailed codec information, including resolution, encod
 
 The corresponding raw dataset in sequence level YUV format to be compressed is generated by the scripts in '../1_generate_raw_data/'.  At the decoder, the dataset, including reconstruction dataset and prediction dataset, is output after LMCS and before DBK.
 
-Especially for the second stage, it is configured to be the codec with the neural network based in-loop filter to generate the B data, which replaces the B data in the dataset of the first stage. Noted that the B data of second stage can also be generated by a codec, whose performance is evaluated to be comparable with the codec enhanced by the latest training model (pseudo-iterative training method).
-
+Especially for the iterative training stage, it is configured to be the codec with the neural network based in-loop filter to generate the B data, which replaces the B data in the dataset of the first stage. Noted that the B data of iterative training stage can also be generated by a codec, whose performance is evaluated to be comparable with the codec enhanced by the latest training model (pseudo-iterative training method).
 ### Macro configurations
-
 Prediction images, slice QPs, base QPs and slice types are fed into the network, besides the reconstruction image before DBF. The following is the specific macro configurations based on the common data dumper. 
 
 ```
@@ -25,7 +23,7 @@ Encoder: --NnlfOption=0
 Decoder: --DumpBasename=/path/AI_DIV2K/dataset
 ```
 
-### Generate the B data of the first stage
+### Generate the B data of the initial training stage
 TVD and BVI-DVC are both compressed under RA configuration. The corresponding encoder and decoder commands are provided as follows. 
 ```
 TVD
@@ -35,8 +33,7 @@ BVI-DVC
 Encoder: --NnlfOption=0
 Decoder: --DumpBasename=/path/RA_BVI_DVC/dataset 
 ```
-
-### Generate the B data of the second stage
+### Generate the B data of the iterative training stage
 TVD and BVI-DVC are both compressed under RA configuration. The corresponding encoder and decoder commands are provided as follows.
 ```
 TVD
@@ -47,7 +44,6 @@ Encoder: --NnlfOption=1
 Decoder: --DumpBasename=/path/RA_BVI_DVC/dataset 
 ```
 
-
 ## The file structure of the compression dataset
 When the compression dataset generation is finished, the file structure should be adjusted to the one as follows.
 
@@ -109,9 +105,14 @@ When the compression dataset generation is finished, the file structure should b
 			│   ...
 ```
 
-## Generate the dataset json
-Run generate_dataset_json.sh to generate the training and test dataset json files. Before running this script, concatenate_dataset.py, placed in ../../../tools, should firstly be copied into the current directory.
+## Generate the dataset
+Run generate_dataset_json.sh first and then run generate_patch.sh to generate the training and test dataset in patch format. Before this generation process, the following scripts should be copied into the current directory ahead.
+```
+../../../tools/concatenate_dataset.py
+../../../data_loader/data_loader.py
+../../../example/create_unified_dataset.py
+```
 
 ## Other
-Because the neural network based filter is used in the generation process of the second stage dataset, the speed is relative slow if it is run on CPU.
-To reduce the time of the whole process, it is recommended to generate the first stage and the second stage datasets in parallel if pseudo-iterative training method is used. 
+Because the neural network based filter is used in the dataset generation process of the iterative training stage, the speed is relative slow if it is run on CPU.
+To reduce the time of the whole process, it is recommended to generate the datasets for the current training stage and the next training stage in parallel if pseudo-iterative training method is used. 
diff --git a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/bvi_dvc_codec_info.py b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/bvi_dvc_codec_info.py
index e91bee4f21b8f1fea64f4fe84ef3bbd3abd455dd..a393a17cd3a74dfb9dcb31dabe07cc69fc6953a7 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/bvi_dvc_codec_info.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/bvi_dvc_codec_info.py
@@ -199,6 +199,406 @@ SequenceTable = [
   ['B_S198', 'BWatPhoTempleVidevo_1920x1088_50fps_10bit_420.yuv', 1920, 1088, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 4.1],
   ['B_S199', 'BWoodSJTU_1920x1088_60fps_10bit_420.yuv', 1920, 1088, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 4.1],
   ['B_S200', 'BWovenVidevo_1920x1088_25fps_10bit_420.yuv', 1920, 1088, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 4.1],
+  ['C_S001', 'CAdvertisingMassagesBangkokVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S002', 'CAmericanFootballS2Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S003', 'CAmericanFootballS3Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S004', 'CAmericanFootballS4Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S005', 'CAnimalsS11Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S006', 'CAnimalsS1Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S007', 'CBangkokMarketVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S008', 'CBasketballGoalScoredS1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S009', 'CBasketballGoalScoredS2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S010', 'CBasketballS1YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S011', 'CBasketballS2YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S012', 'CBasketballS3YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S013', 'CBoatsChaoPhrayaRiverVidevo_960x544_23fps_10bit_420.yuv', 960, 544, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S014', 'CBobbleheadBVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S015', 'CBookcaseBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S016', 'CBoxingPracticeHarmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S017', 'CBricksBushesStaticBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S018', 'CBricksLeavesBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S019', 'CBricksTiltingBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S020', 'CBubblesPitcherS1BVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S021', 'CBuildingRoofS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S022', 'CBuildingRoofS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S023', 'CBuildingRoofS3IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S024', 'CBuildingRoofS4IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S025', 'CBuntingHangingAcrossHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S026', 'CBusyHongKongStreetVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S027', 'CCalmingWaterBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S028', 'CCarpetPanAverageBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S029', 'CCatchBVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S030', 'CCeramicsandSpicesMoroccoVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S031', 'CCharactersYonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S032', 'CChristmasPresentsIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S033', 'CChristmasRoomDareful_960x544_29fps_10bit_420.yuv', 960, 544, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S034', 'CChurchInsideMCLJCV_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S035', 'CCityScapesS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S036', 'CCityScapesS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S037', 'CCityScapesS3IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S038', 'CCityStreetS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S039', 'CCityStreetS3IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S040', 'CCityStreetS4IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S041', 'CCityStreetS5IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S042', 'CCityStreetS6IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S043', 'CCityStreetS7IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S044', 'CCloseUpBasketballSceneVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S045', 'CCloudsStaticBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S046', 'CColourfulDecorationWatPhoVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S047', 'CColourfulKoreanLanternsVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S048', 'CColourfulPaperLanternsVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S049', 'CColourfulRugsMoroccoVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S050', 'CConstructionS2YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S051', 'CCostaRicaS3Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S052', 'CCrosswalkHarmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S053', 'CCrosswalkHongKong2S1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S054', 'CCrosswalkHongKong2S2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S055', 'CCrosswalkHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S056', 'CCrowdRunMCLV_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S057', 'CCyclistS1BVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S058', 'CCyclistVeniceBeachBoardwalkVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S059', 'CDollsScene1YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S060', 'CDollsScene2YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S061', 'CDowntownHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S062', 'CDrivingPOVHarmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S063', 'CDropsOnWaterBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S064', 'CElFuenteMaskLIVENetFlix_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S065', 'CEnteringHongKongStallS1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S066', 'CEnteringHongKongStallS2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S067', 'CFerrisWheelTurningVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S068', 'CFireS18Mitch_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S069', 'CFireS21Mitch_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S070', 'CFireS71Mitch_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S071', 'CFirewoodS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S072', 'CFirewoodS2IRIS_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S073', 'CFitnessIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S074', 'CFjordsS1Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S075', 'CFlagShootTUMSVT_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S076', 'CFlowerChapelS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S077', 'CFlowerChapelS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S078', 'CFlyingCountrysideDareful_960x544_29fps_10bit_420.yuv', 960, 544, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S079', 'CFlyingMountainsDareful_960x544_29fps_10bit_420.yuv', 960, 544, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S080', 'CFlyingThroughLAStreetVidevo_960x544_23fps_10bit_420.yuv', 960, 544, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S081', 'CFungusZoomBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S082', 'CGrassBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S083', 'CGrazTowerIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S084', 'CHamsterBVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S085', 'CHarleyDavidsonIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S086', 'CHongKongIslandVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S087', 'CHongKongMarket1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S088', 'CHongKongMarket2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S089', 'CHongKongMarket3S1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S090', 'CHongKongMarket3S2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S091', 'CHongKongMarket4S1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S092', 'CHongKongMarket4S2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S093', 'CHongKongS1Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S094', 'CHongKongS2Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S095', 'CHongKongS3Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S096', 'CHorseDrawnCarriagesVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S097', 'CHorseStaringS1Videvo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S098', 'CHorseStaringS2Videvo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S099', 'CJockeyHarmonics_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S100', 'CJoggersS1BVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S101', 'CJoggersS2BVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S102', 'CKartingIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S103', 'CKoraDrumsVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S104', 'CLakeYonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S105', 'CLampLeavesBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S106', 'CLaundryHangingOverHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S107', 'CLeaves1BVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S108', 'CLeaves3BVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S109', 'CLowLevelShotAlongHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S110', 'CLungshanTempleS1Videvo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S111', 'CLungshanTempleS2Videvo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S112', 'CManMoTempleVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S113', 'CManStandinginProduceTruckVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S114', 'CManWalkingThroughBangkokVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S115', 'CMaplesS1YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S116', 'CMaplesS2YonseiUniversity_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S117', 'CMirabellParkS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S118', 'CMirabellParkS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S119', 'CMobileHarmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S120', 'CMoroccanCeramicsShopVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S121', 'CMoroccanSlippersVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S122', 'CMuralPaintingVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S123', 'CMyanmarS4Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S124', 'CMyanmarS6Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S125', 'CMyeongDongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S126', 'CNewYorkStreetDareful_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S127', 'COrangeBuntingoverHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S128', 'CPaintingTiltingBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S129', 'CParkViolinMCLJCV_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S130', 'CPedestriansSeoulatDawnVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S131', 'CPeopleWalkingS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S132', 'CPersonRunningOutsideVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S133', 'CPillowsTransBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S134', 'CPlasmaFreeBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S135', 'CPresentsChristmasTreeDareful_960x544_29fps_10bit_420.yuv', 960, 544, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S136', 'CReadySetGoS2TampereUniversity_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S137', 'CResidentialBuildingSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S138', 'CRollerCoaster2Netflix_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S139', 'CRunnersSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S140', 'CRuralSetupIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S141', 'CRuralSetupS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S142', 'CScarfSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S143', 'CSeasideWalkIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S144', 'CSeekingMCLV_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S145', 'CSeoulCanalatDawnVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S146', 'CShoppingCentreVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S147', 'CSignboardBoatLIVENetFlix_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S148', 'CSkyscraperBangkokVidevo_960x544_23fps_10bit_420.yuv', 960, 544, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S149', 'CSmokeClearBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S150', 'CSmokeS45Mitch_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S151', 'CSparklerBVIHFR_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S152', 'CSquareAndTimelapseHarmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S153', 'CSquareS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S154', 'CSquareS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S155', 'CStreetArtVidevo_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S156', 'CStreetDancerS1IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S157', 'CStreetDancerS2IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S158', 'CStreetDancerS3IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S159', 'CStreetDancerS4IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S160', 'CStreetDancerS5IRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S161', 'CStreetsOfIndiaS1Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S162', 'CStreetsOfIndiaS2Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S163', 'CStreetsOfIndiaS3Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S164', 'CTaiChiHongKongS1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S165', 'CTaiChiHongKongS2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S166', 'CTaipeiCityRooftops8Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S167', 'CTaipeiCityRooftopsS1Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S168', 'CTaipeiCityRooftopsS2Videvo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S169', 'CTaksinBridgeVidevo_960x544_23fps_10bit_420.yuv', 960, 544, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S170', 'CTallBuildingsSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S171', 'CTennisMCLV_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S172', 'CToddlerFountain2Netflix_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S173', 'CTouristsSatOutsideVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S174', 'CToyCalendarHarmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S175', 'CTrackingDownHongKongSideVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S176', 'CTrackingPastRestaurantVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S177', 'CTrackingPastStallHongKongVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S178', 'CTraditionalIndonesianKecakVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S179', 'CTrafficandBuildingSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S180', 'CTrafficFlowSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S181', 'CTrafficonTasksinBridgeVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S182', 'CTreeWillsBVITexture_960x544_120fps_10bit_420.yuv', 960, 544, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S183', 'CTruckIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S184', 'CTunnelFlagS1Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S185', 'CUnloadingVegetablesVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S186', 'CVegetableMarketS1LIVENetFlix_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S187', 'CVegetableMarketS2LIVENetFlix_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S188', 'CVegetableMarketS3LIVENetFlix_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S189', 'CVegetableMarketS4LIVENetFlix_960x544_30fps_10bit_420.yuv', 960, 544, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S190', 'CVeniceS1Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S191', 'CVeniceS2Harmonics_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S192', 'CVeniceSceneIRIS_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S193', 'CWalkingDownKhaoStreetVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S194', 'CWalkingDownNorthRodeoVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S195', 'CWalkingThroughFootbridgeVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S196', 'CWaterS65Mitch_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S197', 'CWaterS81Mitch_960x544_24fps_10bit_420.yuv', 960, 544, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S198', 'CWatPhoTempleVidevo_960x544_50fps_10bit_420.yuv', 960, 544, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S199', 'CWoodSJTU_960x544_60fps_10bit_420.yuv', 960, 544, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['C_S200', 'CWovenVidevo_960x544_25fps_10bit_420.yuv', 960, 544, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.1],
+  ['D_S001', 'DAdvertisingMassagesBangkokVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S002', 'DAmericanFootballS2Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S003', 'DAmericanFootballS3Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S004', 'DAmericanFootballS4Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S005', 'DAnimalsS11Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S006', 'DAnimalsS1Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S007', 'DBangkokMarketVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S008', 'DBasketballGoalScoredS1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S009', 'DBasketballGoalScoredS2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S010', 'DBasketballS1YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S011', 'DBasketballS2YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S012', 'DBasketballS3YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S013', 'DBoatsChaoPhrayaRiverVidevo_480x272_23fps_10bit_420.yuv', 480, 272, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S014', 'DBobbleheadBVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S015', 'DBookcaseBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S016', 'DBoxingPracticeHarmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S017', 'DBricksBushesStaticBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S018', 'DBricksLeavesBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S019', 'DBricksTiltingBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S020', 'DBubblesPitcherS1BVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S021', 'DBuildingRoofS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S022', 'DBuildingRoofS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S023', 'DBuildingRoofS3IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S024', 'DBuildingRoofS4IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S025', 'DBuntingHangingAcrossHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S026', 'DBusyHongKongStreetVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S027', 'DCalmingWaterBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S028', 'DCarpetPanAverageBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S029', 'DCatchBVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S030', 'DCeramicsandSpicesMoroccoVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S031', 'DCharactersYonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S032', 'DChristmasPresentsIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S033', 'DChristmasRoomDareful_480x272_29fps_10bit_420.yuv', 480, 272, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S034', 'DChurchInsideMCLJCV_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S035', 'DCityScapesS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S036', 'DCityScapesS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S037', 'DCityScapesS3IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S038', 'DCityStreetS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S039', 'DCityStreetS3IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S040', 'DCityStreetS4IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S041', 'DCityStreetS5IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S042', 'DCityStreetS6IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S043', 'DCityStreetS7IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S044', 'DCloseUpBasketballSceneVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S045', 'DCloudsStaticBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S046', 'DColourfulDecorationWatPhoVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S047', 'DColourfulKoreanLanternsVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S048', 'DColourfulPaperLanternsVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S049', 'DColourfulRugsMoroccoVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S050', 'DConstructionS2YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S051', 'DCostaRicaS3Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S052', 'DCrosswalkHarmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S053', 'DCrosswalkHongKong2S1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S054', 'DCrosswalkHongKong2S2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S055', 'DCrosswalkHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S056', 'DCrowdRunMCLV_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S057', 'DCyclistS1BVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S058', 'DCyclistVeniceBeachBoardwalkVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S059', 'DDollsScene1YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S060', 'DDollsScene2YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S061', 'DDowntownHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S062', 'DDrivingPOVHarmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S063', 'DDropsOnWaterBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S064', 'DElFuenteMaskLIVENetFlix_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S065', 'DEnteringHongKongStallS1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S066', 'DEnteringHongKongStallS2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S067', 'DFerrisWheelTurningVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S068', 'DFireS18Mitch_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S069', 'DFireS21Mitch_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S070', 'DFireS71Mitch_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S071', 'DFirewoodS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S072', 'DFirewoodS2IRIS_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S073', 'DFitnessIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S074', 'DFjordsS1Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S075', 'DFlagShootTUMSVT_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S076', 'DFlowerChapelS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S077', 'DFlowerChapelS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S078', 'DFlyingCountrysideDareful_480x272_29fps_10bit_420.yuv', 480, 272, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S079', 'DFlyingMountainsDareful_480x272_29fps_10bit_420.yuv', 480, 272, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S080', 'DFlyingThroughLAStreetVidevo_480x272_23fps_10bit_420.yuv', 480, 272, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S081', 'DFungusZoomBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S082', 'DGrassBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S083', 'DGrazTowerIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S084', 'DHamsterBVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S085', 'DHarleyDavidsonIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S086', 'DHongKongIslandVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S087', 'DHongKongMarket1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S088', 'DHongKongMarket2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S089', 'DHongKongMarket3S1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S090', 'DHongKongMarket3S2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S091', 'DHongKongMarket4S1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S092', 'DHongKongMarket4S2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S093', 'DHongKongS1Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S094', 'DHongKongS2Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S095', 'DHongKongS3Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S096', 'DHorseDrawnCarriagesVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S097', 'DHorseStaringS1Videvo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S098', 'DHorseStaringS2Videvo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S099', 'DJockeyHarmonics_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S100', 'DJoggersS1BVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S101', 'DJoggersS2BVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S102', 'DKartingIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S103', 'DKoraDrumsVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S104', 'DLakeYonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S105', 'DLampLeavesBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S106', 'DLaundryHangingOverHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S107', 'DLeaves1BVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S108', 'DLeaves3BVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S109', 'DLowLevelShotAlongHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S110', 'DLungshanTempleS1Videvo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S111', 'DLungshanTempleS2Videvo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S112', 'DManMoTempleVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S113', 'DManStandinginProduceTruckVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S114', 'DManWalkingThroughBangkokVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S115', 'DMaplesS1YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S116', 'DMaplesS2YonseiUniversity_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S117', 'DMirabellParkS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S118', 'DMirabellParkS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S119', 'DMobileHarmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S120', 'DMoroccanCeramicsShopVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S121', 'DMoroccanSlippersVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S122', 'DMuralPaintingVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S123', 'DMyanmarS4Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S124', 'DMyanmarS6Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S125', 'DMyeongDongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S126', 'DNewYorkStreetDareful_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S127', 'DOrangeBuntingoverHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S128', 'DPaintingTiltingBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S129', 'DParkViolinMCLJCV_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S130', 'DPedestriansSeoulatDawnVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S131', 'DPeopleWalkingS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S132', 'DPersonRunningOutsideVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S133', 'DPillowsTransBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S134', 'DPlasmaFreeBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S135', 'DPresentsChristmasTreeDareful_480x272_29fps_10bit_420.yuv', 480, 272, 0, 64, 29, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S136', 'DReadySetGoS2TampereUniversity_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S137', 'DResidentialBuildingSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S138', 'DRollerCoaster2Netflix_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S139', 'DRunnersSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S140', 'DRuralSetupIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S141', 'DRuralSetupS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S142', 'DScarfSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S143', 'DSeasideWalkIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S144', 'DSeekingMCLV_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S145', 'DSeoulCanalatDawnVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S146', 'DShoppingCentreVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S147', 'DSignboardBoatLIVENetFlix_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S148', 'DSkyscraperBangkokVidevo_480x272_23fps_10bit_420.yuv', 480, 272, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S149', 'DSmokeClearBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S150', 'DSmokeS45Mitch_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S151', 'DSparklerBVIHFR_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S152', 'DSquareAndTimelapseHarmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S153', 'DSquareS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S154', 'DSquareS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S155', 'DStreetArtVidevo_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S156', 'DStreetDancerS1IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S157', 'DStreetDancerS2IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S158', 'DStreetDancerS3IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S159', 'DStreetDancerS4IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S160', 'DStreetDancerS5IRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S161', 'DStreetsOfIndiaS1Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S162', 'DStreetsOfIndiaS2Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S163', 'DStreetsOfIndiaS3Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S164', 'DTaiChiHongKongS1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S165', 'DTaiChiHongKongS2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S166', 'DTaipeiCityRooftops8Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S167', 'DTaipeiCityRooftopsS1Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S168', 'DTaipeiCityRooftopsS2Videvo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S169', 'DTaksinBridgeVidevo_480x272_23fps_10bit_420.yuv', 480, 272, 0, 64, 23, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S170', 'DTallBuildingsSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S171', 'DTennisMCLV_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S172', 'DToddlerFountain2Netflix_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S173', 'DTouristsSatOutsideVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S174', 'DToyCalendarHarmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S175', 'DTrackingDownHongKongSideVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S176', 'DTrackingPastRestaurantVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S177', 'DTrackingPastStallHongKongVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S178', 'DTraditionalIndonesianKecakVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S179', 'DTrafficandBuildingSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S180', 'DTrafficFlowSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S181', 'DTrafficonTasksinBridgeVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S182', 'DTreeWillsBVITexture_480x272_120fps_10bit_420.yuv', 480, 272, 0, 64, 120, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S183', 'DTruckIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S184', 'DTunnelFlagS1Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S185', 'DUnloadingVegetablesVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S186', 'DVegetableMarketS1LIVENetFlix_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S187', 'DVegetableMarketS2LIVENetFlix_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S188', 'DVegetableMarketS3LIVENetFlix_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S189', 'DVegetableMarketS4LIVENetFlix_480x272_30fps_10bit_420.yuv', 480, 272, 0, 64, 30, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S190', 'DVeniceS1Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S191', 'DVeniceS2Harmonics_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S192', 'DVeniceSceneIRIS_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S193', 'DWalkingDownKhaoStreetVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S194', 'DWalkingDownNorthRodeoVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S195', 'DWalkingThroughFootbridgeVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S196', 'DWaterS65Mitch_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S197', 'DWaterS81Mitch_480x272_24fps_10bit_420.yuv', 480, 272, 0, 64, 24, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S198', 'DWatPhoTempleVidevo_480x272_50fps_10bit_420.yuv', 480, 272, 0, 64, 50, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S199', 'DWoodSJTU_480x272_60fps_10bit_420.yuv', 480, 272, 0, 64, 60, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],
+  ['D_S200', 'DWovenVidevo_480x272_25fps_10bit_420.yuv', 480, 272, 0, 64, 25, 10, '22 27 32 37 42', 'R22 R27 R32 R37 R42', 3.0],  
 ]
 
 
diff --git a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_dataset_json.sh b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_dataset_json.sh
index 47f29849752d8cc6b9be9a143d817d8a9ba7e6d1..5b78e5d9dc75cb425e0253645eccc6dcde1586eb 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_dataset_json.sh
+++ b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_dataset_json.sh
@@ -29,39 +29,37 @@
 # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 # THE POSSIBILITY OF SUCH DAMAGE.
-
-
 mkdir train test
 
-python concatenate_dataset.py --input_dir_json /path/AI_DIV2K/dataset --dataset_range 0-4000 --output_json pre.json
+python concatenate_dataset.py --input_dir_json /path/AI_DIV2K/dataset --dataset_range 1-800 --output_json pre.json
 python concatenate_dataset.py --input_dir_encoder /path/AI_DIV2K/enc_log --input_json pre.json --output_json /path/train/info_AI_DIV2K.json
 
 rm pre.json
 
-python concatenate_dataset.py --input_dir_json /path/AI_DIV2K/dataset --dataset_range 4000-4500 --output_json pre.json
+python concatenate_dataset.py --input_dir_json /path/AI_DIV2K/dataset --dataset_range 801-900 --output_json pre.json
 python concatenate_dataset.py --input_dir_encoder /path/AI_DIV2K/enc_log --input_json pre.json --output_json /path/test/info_AI_DIV2K.json
 
 rm pre.json
 
-python concatenate_dataset.py --input_dir_json /path/RA_TVD/dataset --dataset_range 0-330 --output_json pre.json
+python concatenate_dataset.py --input_dir_json /path/RA_TVD/dataset --dataset_range 1-18 --output_json pre.json
 python concatenate_dataset.py --input_dir_encoder /path/RA_TVD/enc_log --input_json pre.json --output_json /path/train/info_RA_TVD.json
 
 rm pre.json
 
-python concatenate_dataset.py --input_dir_json /path/RA_TVD/dataset --dataset_range 330-370 --output_json pre.json
+python concatenate_dataset.py --input_dir_json /path/RA_TVD/dataset --dataset_range 19-20 --output_json pre.json
 python concatenate_dataset.py --input_dir_encoder /path/RA_TVD/enc_log --input_json pre.json --output_json /path/test/info_RA_TVD.json
 
 rm pre.json
 
-python concatenate_dataset.py --input_dir_json /path/RA_BVI_DVC/dataset --dataset_range 0-900 --output_json pre.json
+python concatenate_dataset.py --input_dir_json /path/RA_BVI_DVC/dataset --dataset_range 1-180 --output_json pre.json
 python concatenate_dataset.py --input_dir_encoder /path/RA_BVI_DVC/enc_log --input_json pre.json --output_json /path/train/info_RA_BVI_DVC.json
 
 rm pre.json
 
-python concatenate_dataset.py --input_dir_json /path/RA_BVI_DVC/dataset --dataset_range 900-1000 --output_json pre.json
+python concatenate_dataset.py --input_dir_json /path/RA_BVI_DVC/dataset --dataset_range 181-200 --output_json pre.json
 python concatenate_dataset.py --input_dir_encoder /path/RA_BVI_DVC/enc_log --input_json pre.json --output_json /path/test/info_RA_BVI_DVC.json
 
 rm pre.json
 
 python concatenate_dataset.py --input_dir_json /path/train --output_json /path/train/train.json
-python concatenate_dataset.py --input_dir_json /path/test --output_json /path/test/test.json
+python concatenate_dataset.py --input_dir_json /path/test --output_json /path/test/test.json
\ No newline at end of file
diff --git a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_patch.sh b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_patch.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e90880317ded406fb6d4638b62d8bd3256e57db2
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/generate_patch.sh
@@ -0,0 +1,41 @@
+# The copyright in this software is being made available under the BSD
+# License, included below. This software may be subject to other third party
+# and contributor rights, including patent rights, and no such rights are
+# granted under this license.
+#
+# Copyright (c) 2010-2022, ITU/ISO/IEC
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#  * Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+#  * Neither the name of the ITU/ISO/IEC nor the names of its contributors may
+#    be used to endorse or promote products derived from this software without
+#    specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS
+# BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+path_cur=$(cd $(dirname $0); pwd)
+
+luma_comp="org_Y,rec_before_dbf_Y,pred_Y,qp_slice,qp_base,slice_type"
+chroma_comp="org_U,org_V,rec_before_dbf_U,rec_before_dbf_V,pred_U,pred_V"
+
+for type in train test
+do
+python create_unified_dataset.py --generate_type 0 --random_sample 0 --poc_list --nb_patches -1 --patch_size 128 --border_size 8 --input_json $path_cur/$type/$type.json --output_file $path_cur/$type/luma.bin   --components $luma_comp
+python create_unified_dataset.py --generate_type 0 --random_sample 0 --poc_list --nb_patches -1 --patch_size 128 --border_size 8 --input_json $path_cur/$type/$type.json --output_file $path_cur/$type/chroma.bin --components $chroma_comp
+done
diff --git a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/tvd_codec_info.py b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/tvd_codec_info.py
index 113635942907a1040ab906675ff1ca94d1a50c68..688ae24303cf9f9ba4f7e31cde5815a2bfa0ec33 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/tvd_codec_info.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/2_generate_compression_data/tvd_codec_info.py
@@ -19,60 +19,6 @@ SequenceTable = [
   ['A_S18', 'ChefCuttingUp2_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
   ['A_S19', 'DryRedPepper_3840x2160_25fps_10bit_420.yuv',               3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
   ['A_S20', 'FilmMachine_3840x2160_25fps_10bit_420.yuv',                3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S21', 'FlowingWater_3840x2160_50fps_10bit_420.yuv',               3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S22', 'Fountain_3840x2160_50fps_10bit_420.yuv',                   3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S23', 'GirlRunningOnGrass_3840x2160_50fps_10bit_420.yuv',         3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S24', 'GirlWithTeaSet1_3840x2160_25fps_10bit_420.yuv',            3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S25', 'GirlWithTeaSet2_3840x2160_25fps_10bit_420.yuv',            3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S26', 'GirlWithTeaSet3_3840x2160_25fps_10bit_420.yuv',            3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S27', 'GirlsOnGrass1_3840x2160_50fps_10bit_420.yuv',              3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S28', 'GirlsOnGrass2_3840x2160_50fps_10bit_420.yuv',              3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S29', 'HotPot_3840x2160_25fps_10bit_420.yuv',                     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S30', 'HotelClerks_3840x2160_25fps_10bit_420.yuv',                3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S31', 'LyingDog_3840x2160_25fps_10bit_420.yuv',                   3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S32', 'ManWithFilmMachine_3840x2160_25fps_10bit_420.yuv',         3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S33', 'MountainsAndStairs1_3840x2160_24fps_10bit_420.yuv',        3840, 2160, 0,  65,  24,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S34', 'MountainsAndStairs2_3840x2160_25fps_10bit_420.yuv',        3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S35', 'MountainsAndStairs3_3840x2160_25fps_10bit_420.yuv',        3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S36', 'MountainsAndStairs4_3840x2160_25fps_10bit_420.yuv',        3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S37', 'MountainsView1_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S38', 'MountainsView2_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S39', 'MountainsView3_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S40', 'MountainsView4_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S41', 'MovingBikesAndPedestrian4_3840x2160_50fps_10bit_420.yuv',  3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S42', 'OilPainting1_3840x2160_25fps_10bit_420.yuv',               3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S43', 'OilPainting2_3840x2160_25fps_10bit_420.yuv',               3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S44', 'PeopleNearDesk_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S45', 'PeopleOnGrass_3840x2160_50fps_10bit_420.yuv',              3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S46', 'Plaque_3840x2160_25fps_10bit_420.yuv',                     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S47', 'PressureCooker_3840x2160_25fps_10bit_420.yuv',             3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S48', 'RawDucks_3840x2160_25fps_10bit_420.yuv',                   3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S49', 'RedBush_3840x2160_25fps_10bit_420.yuv',                    3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S50', 'RedRibbonsWithLocks_3840x2160_25fps_10bit_420.yuv',        3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S51', 'RestaurantWaitress1_3840x2160_25fps_10bit_420.yuv',        3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S52', 'RestaurantWaitress2_3840x2160_25fps_10bit_420.yuv',        3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S53', 'RiverAndTrees_3840x2160_25fps_10bit_420.yuv',              3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S54', 'RoastedDuck_3840x2160_25fps_10bit_420.yuv',                3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S55', 'RoomTouristAttraction1_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S56', 'RoomTouristAttraction2_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S57', 'RoomTouristAttraction3_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S58', 'RoomTouristAttraction4_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S59', 'RoomTouristAttraction5_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S60', 'RoomTouristAttraction6_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S61', 'RoomTouristAttraction7_3840x2160_25fps_10bit_420.yuv',     3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S62', 'StampCarving1_3840x2160_25fps_10bit_420.yuv',              3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S63', 'StampCarving2_3840x2160_25fps_10bit_420.yuv',              3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S64', 'StaticRocks_3840x2160_50fps_10bit_420.yuv',                3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S65', 'StaticWaterAndBikes2_3840x2160_50fps_10bit_420.yuv',       3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S66', 'SunAndTrees_3840x2160_25fps_10bit_420.yuv',                3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S67', 'SunriseMountainHuang_3840x2160_25fps_10bit_420.yuv',       3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S68', 'SunsetMountainHuang1_3840x2160_25fps_10bit_420.yuv',       3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S69', 'SunsetMountainHuang2_3840x2160_25fps_10bit_420.yuv',       3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S70', 'TreesAndLeaves_3840x2160_50fps_10bit_420.yuv',             3840, 2160, 0,  65,  50,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S71', 'TreesOnMountains1_3840x2160_25fps_10bit_420.yuv',          3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S72', 'TreesOnMountains2_3840x2160_25fps_10bit_420.yuv',          3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S73', 'TreesOnMountains3_3840x2160_25fps_10bit_420.yuv',          3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
-  ['A_S74', 'Weave_3840x2160_25fps_10bit_420.yuv',                      3840, 2160, 0,  65,  25,  10, '22 27 32 37 42', 'R22 R27 R32 R37 R42',  4.1],
 ]
 
 
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/3_ReadMe.md b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/3_ReadMe.md
index 81cf5959912bc8230b951101868d252a625e6194..0847180f419e45fa6100cda6e4614a3958586bba 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/3_ReadMe.md
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/3_ReadMe.md
@@ -10,43 +10,29 @@ tensorboard --logdir=Tensorboard --port=6003
 2. Access http://localhost:6003/ to view the result
 
 ### Training configuration
-In this training process, the learning rate is set to 1e-4, and the L1 loss function is used. 
+In this training process, the learning rate is set to 1e-4. Both L1 and L2 loss function are used. 
 
 The patch size is set to 128 for luma component, and the corresponding padding size is set to 8. That is, the real patch size fed into network is 144. Noted that the selected patch is aligned to the CTU boundary in the training script. In addition, data augmentation, such as horizontal flipping, vertical flipping, and rotation, is used.
 
-In the conventional training process, one epoch feeds all the dataset to the network for training once. Different from the conventional training process, in this training process, only about 0.6% of dataset is fed into network for training once per epoch, where only one patch randomly selected from each frame is used per epoch. That is, the amount of data input to the network for 167 such epochs is the amount of data input to the network for 1 conventional epoch. 
+### Initial training stage
+The root path of the dataset in patch format, used for training and test, should be set in Utils.py by configuring the '--root_path' option.
 
-### The first training stage
-Before launching the training process, data_loader.py, placed in ../../../../data_loader, should firstly be copied into ./training_scripts.
-Launch the first training stage by the following command:
+Launch the training stage by the following command:
 ```
 sh ./training_scripts/start_training.sh
 ```
 
-The convergence curve on the validation set is shown below.
-![](../figure/convergence_curve_1_stage.jpg)
-
-The convergence curve above is enlarged to select the optimal model as follows. The model selection is decided based on the PSNR improvement of Y, Cb and Cr. 
-For example, the model generated by the 1488th epoch is selected as the optimal result in this stage.
-![](../figure/model_selection_1_stage.jpg)
-
-### The second training stage
-
-The difference of training scripts between the first stage and the second stage is as follows.
-* line 32, Utils.py: load the pre-trained model, placed in ./Experiments/Checkpoints/, generated by the first stage
-
-The dataset json, generated by the I data and the B data of the second stage, should be loaded (line 27 and line 28, Utils.py).
-
-Similar to the first training stage, the optimal model can be picked out from the training results. 
+### Iterative training stage
+1. Revise the dataset path as in the initial training stage ('--root_path' option, Utils.py)
+2. Load the pretrained checkpoint from the initial training stage ('--pretrained' option, Utils.py)
 
 ### Convert to SADL model
-1. Select the optimal model of the second training stage as the final model.
+1. Select the optimal model in the last training stage as the final model.
 2. Use the following command to finish the model conversion, then NnlfSet0_model_float.sadl and NnlfSet0_model_int16.sadl can be generated. Noted that the model to be converted should be firstly set in the line 9 of model_conversion.py. In addition, naive_quantization file should be complied, according to the SADL guidance, and placed in ./model_conversion before run conversion_to_sadl.sh.
 ```
 sh ./model_conversion/conversion_to_sadl.sh
 ```
 ### Other
-Empirically, it is best to perform training until the PSNR results on the validation set begin to decline.
-So for the provided training scripts, a fixed and relative large 2000 training epochs is set. In this training process, the training can be killed when the curve on the validation set seems to be converging. In fact, most of performance can be achieved with less epoches training in the early stage, and the performance increase in later stage is very small.
-Because of the randomness in the training process, the number of training epochs provided here, such as 1488th in the first training stage, is only for the reference.
+Empirically, it is best to perform training until the PSNR results on the validation set begin to decline. So for the provided training scripts, a fixed and relative large 500 training epochs is set. 
+In this training process, the training can be killed when the curve on the validation set seems to be converging.
 In my experience, the model with the best PSNR for Y and relatively good PSNR for Cb and Cr is usually selected.
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/conversion_to_sadl.sh b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/conversion_to_sadl.sh
index 248732071bce776ae78b11ae21371ef7c887d5a9..4ef274dc4961f1937bfed0776f915e6a6a249d2a 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/conversion_to_sadl.sh
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/conversion_to_sadl.sh
@@ -29,10 +29,8 @@
 # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 # THE POSSIBILITY OF SUCH DAMAGE.
-
-
 python model_conversion.py
-python main.py --input_onnx IB_model.onnx --output NnlfSet0_model_float.sadl
+python main.py --input_onnx NnlfSet0_model.onnx --output NnlfSet0_model_float.sadl
 
 echo "[INFO] WRITE INT16 MODEL"
 tail -n 1 Q.log | ./naive_quantization NnlfSet0_model_float.sadl NnlfSet0_model_int16.sadl
\ No newline at end of file
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/model_conversion.py b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/model_conversion.py
index 291a3454c1f386814b4291dd83aab22b3c4a9a54..b9991b86c89d6c10da3ff30d4b65d6f4818da446 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/model_conversion.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/model_conversion/model_conversion.py
@@ -39,7 +39,7 @@ from argparse import ArgumentParser
 
 if __name__ == '__main__':
     model = Net()
-    model_name = 'model_1000.pth'
+    model_name = 'model_100.pth'
     model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage)["network"])
     model.eval()
 
@@ -61,4 +61,4 @@ if __name__ == '__main__':
 
     output = model(input0, input0, input1, input1, input1)
     print("Output",output)
-    torch.onnx.export(model, (input0, input0, input1, input1, input1), "./IB_model.onnx") 
+    torch.onnx.export(model, (input0, input0, input1, input1, input1), "./NnlfSet0_model.onnx") 
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/Utils.py b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/Utils.py
index 248b59aa03323e9f729eceaa5e71ca7975ca1988..9b690421af04019ab455efcb6759d4493901c51d 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/Utils.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/Utils.py
@@ -31,7 +31,6 @@
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 * THE POSSIBILITY OF SUCH DAMAGE.
 """
-
 import argparse
 import datetime
 import logging
@@ -58,8 +57,7 @@ def parse_args():
     path_save = path_cur.joinpath("Experiments")
 
     # for loading data
-    parser.add_argument("--json_train", type=str, default='/path/train/train.json', help="train json.")
-    parser.add_argument("--json_test", type=str, default='/path/test/test.json', help="test json.")
+    parser.add_argument("--root_path", type=str, default='/path', help="the root path of dataset.")
 
     # for loading model
     parser.add_argument("--checkpoints", type=str, help="checkpoints file path")
@@ -67,8 +65,6 @@ def parse_args():
 
     # batch size
     parser.add_argument("--batch_size", type=int, default=64, help="batch size for Fusion stage")
-    # do validation
-    parser.add_argument("--test_every",type=int, default=1200, help="do test per every N batches")
 
     # learning rate
     parser.add_argument("--lr", type=float, default=1e-4, help="learning rate for Fusion stage")
@@ -76,7 +72,7 @@ def parse_args():
     parser.add_argument("--gpu", action='store_true', default=True, help="use gpu or cpu")
 
     # epoch
-    parser.add_argument("--max_epoch", type=int, default=2000, help="max training epochs")
+    parser.add_argument("--max_epoch", type=int, default=500, help="max training epochs")
 
     # patch_size
     parser.add_argument("--patch_size", type=int, default=128, help="train/val patch size")
@@ -85,7 +81,7 @@ def parse_args():
     # for recording
     parser.add_argument("--verbose", action='store_true', default=True, help="use tensorboard and logger")
     parser.add_argument("--save_dir", type=str, default=path_save, help="directory for recording")
-    parser.add_argument("--eval_epochs", type=int, default=2, help="save model after epochs")
+    parser.add_argument("--eval_epochs", type=int, default=1, help="save model after epochs")
 
     args = parser.parse_args()
     return args
@@ -147,26 +143,17 @@ def uv_upsample(img_uv):
     ret = np.concatenate((u_up, v_up), axis=2)
     return ret
    
-def get_patch(data_luma, data_chroma, side_info):
-    data_luma = np.squeeze(data_luma, axis = 0)
-    data_chroma = np.squeeze(data_chroma, axis = 0)
-    side_info = np.squeeze(side_info, axis = 0)
-
-    org_y = data_luma[:,:,0:1]
-    rec_y = data_luma[:,:,1:2]
-    pre_y = data_luma[:,:,2:3]
+def get_patch(data_luma, data_chroma):
+    org_y      = data_luma[:,:,0:1]
+    rec_y      = data_luma[:,:,1:2]
+    pre_y      = data_luma[:,:,2:3]
+    slice_qp   = data_luma[:,:,3:4]
+    base_qp    = data_luma[:,:,4:5]
+    slice_type = data_luma[:,:,5:6]
     
     org_uv = uv_upsample(data_chroma[:,:,0:2])
     rec_uv = uv_upsample(data_chroma[:,:,2:4])
     pre_uv = uv_upsample(data_chroma[:,:,4:6])
-    
-    slice_qp   = side_info[:,:,0:1]
-    base_qp    = side_info[:,:,1:2]
-    slice_type = side_info[:,:,2:3]
-    
-    slice_type[slice_type!=2] = 1023
-    slice_type[slice_type==2] = 0
-    slice_type/=1023.
 
     patch_in = np.concatenate((rec_y, rec_uv, pre_y, pre_uv, slice_qp, base_qp, slice_type), axis=2) 
     patch_org = np.concatenate((org_y, org_uv), axis=2)
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/start_training.sh b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/start_training.sh
index 319fd0cdaabd3dedf2254f70ce5e3cb484cc0751..adca1c63d87e96406c997e8edc291d9cbbdc4d73 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/start_training.sh
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/start_training.sh
@@ -29,5 +29,4 @@
 # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 # THE POSSIBILITY OF SUCH DAMAGE.
-
 CUDA_VISIBLE_DEVICES=0 python train_YUV.py
\ No newline at end of file
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/train_YUV.py b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/train_YUV.py
index b464cfac616c3e7b6d5a350d9c708935060eb35c..33e30e42a80833bed1ee2d52fa884580fb2b8dd2 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/train_YUV.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/train_YUV.py
@@ -31,7 +31,6 @@
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 * THE POSSIBILITY OF SUCH DAMAGE.
 """
-
 import torch
 import torch.nn as nn
 from torch.optim.adam import Adam
@@ -58,14 +57,14 @@ class Trainer:
         self.L2loss = nn.MSELoss().to("cuda" if self.args.gpu else "cpu")
         
         self.optimizer = Adam(self.net.parameters(), lr = self.args.lr)
-        self.scheduler = MultiStepLR(optimizer=self.optimizer, milestones=[2001, 2002], gamma=0.5)
+        self.scheduler = MultiStepLR(optimizer=self.optimizer, milestones=[501, 502], gamma=0.5)
         
         print("============>loading data")
         self.train_dataset = YUV10bData(self.args, train=True)
         self.eval_dataset = YUV10bData(self.args, train=False)
         
         self.train_dataloader = DataLoader(dataset=self.train_dataset, batch_size=self.args.batch_size, shuffle=True, num_workers=12, pin_memory=False)
-        self.eval_dataloader = DataLoader(dataset=self.eval_dataset, batch_size=self.args.batch_size, shuffle=True, num_workers=12, pin_memory=False)
+        self.eval_dataloader = DataLoader(dataset=self.eval_dataset, batch_size=self.args.batch_size, shuffle=False, num_workers=12, pin_memory=False)
         
         self.train_steps = self.eval_steps = 0
 
@@ -108,11 +107,18 @@ class Trainer:
             loss_pred_Cb = self.L2loss(img_out[:,1,shave:-shave,shave:-shave], img_ori[:, 1,shave:-shave,shave:-shave])
             loss_pred_Cr = self.L2loss(img_out[:,2,shave:-shave,shave:-shave], img_ori[:, 2,shave:-shave,shave:-shave])
             
-            loss_pred = 10*L1_loss_pred_Y + L1_loss_pred_Cb + L1_loss_pred_Cr 
+            loss_pred_l1 = 10 * L1_loss_pred_Y + L1_loss_pred_Cb + L1_loss_pred_Cr
+            loss_pred_l2 = 10 * loss_pred_Y + loss_pred_Cb + loss_pred_Cr
             
             loss_rec_Y  = self.L2loss(img_in[:,0,shave:-shave,shave:-shave], img_ori[:, 0,shave:-shave,shave:-shave])
             loss_rec_Cb = self.L2loss(img_in[:,1,shave:-shave,shave:-shave], img_ori[:, 1,shave:-shave,shave:-shave])
             loss_rec_Cr = self.L2loss(img_in[:,2,shave:-shave,shave:-shave], img_ori[:, 2,shave:-shave,shave:-shave])
+
+            #L1 first, and then L1+L2
+            if self.train_steps // len(self.train_dataloader) > 40:
+                loss_pred = 0.1 * loss_pred_l1 + loss_pred_l2
+            else:
+                loss_pred = loss_pred_l1
     
             # visualization
             self.train_steps += 1
@@ -128,8 +134,9 @@ class Trainer:
                 time = datetime.datetime.now().strftime("%Y-%m-%d_%H:%M")
                 print("[{}/{}]\tY:{:.8f}\tCb:{:.8f}\tCr:{:.8f}\tdelta_Y: {:.8f}------{}".format((self.train_steps % len(self.train_dataloader)), len(self.train_dataloader), 
                                             loss_pred_Y, loss_pred_Cb, loss_pred_Cr, psnr_pred_Y - psnr_input_Y, time))
-                self.logger.info("[{}/{}]\tY:{:.8f}\tCb:{:.8f}\tCr:{:.8f}\tdelta_Y: {:.8f}".format((self.train_steps % len(self.train_dataloader)), len(self.train_dataloader), 
-                                            loss_pred_Y, loss_pred_Cb, loss_pred_Cr, psnr_pred_Y - psnr_input_Y))
+                self.logger.info("[{}/{}]\tY_l1:{:.8f}\tY_l2:{:.8f}\tCb:{:.8f}\tCr:{:.8f}\tdelta_Y: {:.8f}".format((self.train_steps % len(self.train_dataloader)), len(self.train_dataloader),
+                                            L1_loss_pred_Y, loss_pred_Y, loss_pred_Cb, loss_pred_Cr, psnr_pred_Y - psnr_input_Y))
+
 
                 self.tensorboard_all.add_scalars(main_tag="Train/PSNR",
                                             tag_scalar_dict={"input_Y": psnr_input_Y.data,
diff --git a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/yuv10bdata.py b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/yuv10bdata.py
index d1f226ca17a6c422859bd59835de25484857f56a..d679d0854bea02d390ff040a701846fa9466deee 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/yuv10bdata.py
+++ b/training/training_scripts/Nn_Filtering_Set_0/3_training_tasks/training_scripts/yuv10bdata.py
@@ -31,107 +31,61 @@
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 * THE POSSIBILITY OF SUCH DAMAGE.
 """
-
 import os
-import glob
 from torch.utils.data import Dataset
-import torch.nn.functional as F
 import numpy as np
-import string
-import random
 import Utils
-import data_loader
-from data_loader import PatchInfo
-from typing import NamedTuple
-
-
-class FrameInfo(NamedTuple):
-    patch_info_index: int
-    index_length:int
-
+import json    
+    
 class YUV10bData(Dataset):
     def __init__(self, args, train=True):
         super(YUV10bData, self).__init__()
-        self.args = args
         self.train = train
-                
-        jsonfile = self.args.json_train if train else self.args.json_test
-        self.dl=data_loader.DataLoader(jsonfile, self.args.patch_size, [], 0)
-        self.frame_table = self.convertPatchInfo(self.dl.patch_info)
-        self.frame_number = len(self.frame_table)
-        print(self.frame_number)
-
-        if train:
-            n_patches = args.batch_size * args.test_every
-            n_images = self.frame_number
-            if n_images == 0:
-                self.repeat = 0
-            else:
-                self.repeat = max(n_patches // n_images, 1)
-            print(f"repeating dataset {self.repeat} for one epoch")
-        else:
-            n_patches = args.batch_size * args.test_every // 25
-            n_images = self.frame_number
-            if n_images == 0:
-                self.repeat = 0
-            else:
-                self.repeat = 5
-            print(f"repeating dataset {self.repeat} for one epoch")
-    
-    def convertPatchInfo(self, patch_info):
-        frame_table = []
-        patch_number = len(patch_info)
-
-        index_start = 0
-        cnt = 0
-        patch_state = 0
-        data_index_hisroty = -1
-        frame_index_hisroty = -1
-        for idx in range(patch_number):
-            if patch_info[idx].data_index == data_index_hisroty and patch_info[idx].frame_index == frame_index_hisroty:
-                cnt += 1
-                if idx == patch_number-1:
-                    frame_table.append(FrameInfo(index_start, cnt))
-            else:
-                if idx != 0:
-                    frame_table.append(FrameInfo(index_start, cnt))
-                index_start = idx
-                data_index_hisroty = patch_info[idx].data_index
-                frame_index_hisroty = patch_info[idx].frame_index
-                cnt = 1
-        return frame_table
-    
+        
+        leaf_path = 'train' if self.train else 'test'
+        json_luma   = os.path.join(args.root_path, leaf_path, 'luma.bin.json')
+        json_chroma = os.path.join(args.root_path, leaf_path, 'chroma.bin.json')
+        
+        self.luma_json_content, self.luma_number = self.readJson(json_luma)
+        self.chroma_json_content, self.chroma_number = self.readJson(json_chroma)                 
+        
+        assert(self.luma_number == self.chroma_number)
+        
+        self.patch_number = self.luma_number
+        print('patch number of training:' if self.train else '    patch number of test:', self.patch_number)
+       
+    def readJson(self, jsonfile):
+        with open(jsonfile, "r") as file:
+            content = file.read()
+            dcontent = json.loads(content)
+            c=dcontent['nb_patches']
+            return dcontent, c
+        
     def __getitem__(self, idx):
         patch_in, patch_org = self.load_file_get_patch(idx)
         pair_t = Utils.np2Tensor(patch_in, patch_org)
         
         return pair_t[0], pair_t[1]
 
+    def readPatch(self, dcontent, idx):
+        file=open(dcontent['data'],"r")
+        s=dcontent['patch_size']
+        b=dcontent['border_size']
+        d=len(dcontent['components'])
+        sb=s+2*b
+        unit_length=sb*sb*d
+        t=np.fromfile(file,dtype='float32',count=unit_length,offset=idx*unit_length*4).reshape((sb,sb,d))
+        file.close()
+        return t
+        
     def __len__(self):
-        return self.frame_number * self.repeat
-
-    def get_index(self, idx):
-        idx = idx % self.frame_number
-        idx = self.frame_table[idx].patch_info_index + random.randint(0, self.frame_table[idx].index_length-1)
-        return idx
+        return self.patch_number
 
     def load_file_get_patch(self, idx):
-            
-        idx = self.get_index(idx)
-        
-        comps_data_luma = "org_Y,rec_before_dbf_Y,pred_Y" 
-        comps_data_luma = comps_data_luma.split(",")
-        comps_data_chroma = "org_U,org_V,rec_before_dbf_U,rec_before_dbf_V,pred_U,pred_V" 
-        comps_data_chroma = comps_data_chroma.split(",")
-        comps_side_info = "qp_slice,qp_base,slice_type"
-        comps_side_info = comps_side_info.split(",")        
-        
-        data_luma = self.dl.getPatchData(idx, comps_data_luma, self.args.shave)
-        data_chroma = self.dl.getPatchData(idx, comps_data_chroma, self.args.shave)
-        side_info = self.dl.getPatchData(idx, comps_side_info, self.args.shave)
-        
-        patch_in, patch_org = Utils.get_patch(data_luma, data_chroma, side_info)
-        
+        assert(idx<self.patch_number)
+        data_luma = self.readPatch(self.luma_json_content, idx)
+        data_chroma = self.readPatch(self.chroma_json_content, idx)
+        patch_in, patch_org = Utils.get_patch(data_luma, data_chroma)
         
         if self.train:
             patch_in, patch_org  = Utils.augment(patch_in, patch_org)
diff --git a/training/training_scripts/Nn_Filtering_Set_0/ReadMe.md b/training/training_scripts/Nn_Filtering_Set_0/ReadMe.md
index da7a2ff6382780face170a18b942ee20c4f67189..6252bf57b533b422bbae24fb78f081f012f90b8e 100644
--- a/training/training_scripts/Nn_Filtering_Set_0/ReadMe.md
+++ b/training/training_scripts/Nn_Filtering_Set_0/ReadMe.md
@@ -2,17 +2,11 @@
 
 Requirements:
 * One GPU with greater than 25GiB memory.
-* Preferably the disk storage size is greater than 2.1TB, which is the approximate total size of all the related data of first training stage, including the raw data.
+* Preferably the disk storage size is greater than 1TB.
 
 The overview of relationships among these directories is shown below:
-* 1_generate_raw_data
-* 2_generate_compression_data
-* 3_training_tasks
-![](./figure/overview.jpg)
+* 1_generate_raw_data: generate raw dataset.
+* 2_generate_compression_data: raw dataset compression and generate training dataset and test dataset in patch format.
+* 3_training_tasks: training process and model conversion.
 
-Regarding the training process, (pseudo) iterative training method is used. The brief introduction can be found in the attached pdf document (Brief introduction to the training process.pdf).
-
-Furthermore, the provided training scripts are implemented by loading the patches on-the-fly. Compared with the off-line loading method, this on-the-fly loading method requires less disk storage size because it does not need to generate another dedicated patch file. Therefore, there is no need to generate this dedicated patch file again if the needed dataset need to be revised. In the case of revising dataset, just regenerating the dataset json file can meet this requirement. BTW, several experiments show that there is no obvious speed difference between on-the-fly loading method and the off-line loading method.
-
-For the better viewing experience, please open those md files with [typora](https://typora.io/). 
-Certainly, the guidance can be viewed as a general txt file, but the figure inside can not be displayed directly. In this case, you can find the needed figure in the figure folder and view it individually. 
\ No newline at end of file
+Regarding the training process, (pseudo) iterative training method is used. The brief introduction can be found in the attached pdf document (Brief introduction to the training process.pdf).
\ No newline at end of file
diff --git a/training/training_scripts/Nn_Filtering_Set_0/figure/convergence_curve_1_stage.jpg b/training/training_scripts/Nn_Filtering_Set_0/figure/convergence_curve_1_stage.jpg
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