中国科学技术大学：《多媒体通信 Multimedia Communications》课程教学资源（文献阅读交流）Learning to Predict Streaming Video QoE Distortions, Rebuffering and Memory

Learning to Predict Streaming Video QoE:Distortions, Rebuffering and Memory Christos G.Bampis,Student Member,IEEE,and Alan C.Bovik,Fellow,IEEE SA19006037 Jianzhao Liu

Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory Christos G. Bampis, Student Member, IEEE, and Alan C. Bovik, Fellow, IEEE SA19006037 Jianzhao Liu

Motivation For streaming applications,adaptive network strategies may involve a combination of dynamic bitrate allocation along with playback interruptions when the available bandwidth reaches a very low value. Propose Video Assessment of TemporaL Artifacts and Stalls (Video ATLAS):a machine learning framework where we combine a number of QoE-related features,including objective quality features,rebuffering- aware features and memory-driven features to make QoE predictions

Motivation For streaming applications, adaptive network strategies may involve a combination of dynamic bitrate allocation along with playback interruptions when the available bandwidth reaches a very low value. Propose Video Assessment of TemporaLArtifacts and Stalls (Video ATLAS): a machine learning framework where we combine a number of QoE-related features, including objective quality features, rebuffering￾aware features and memory-driven features to make QoE predictions

Previous Work on QoE Prediction Impairments of Videos with Normal Playback Playback interruptions Due to the multiple encoding bitstream Due to throughput and buffer limitations representations of the high-quality source content SSIM、MS-SSIM、VMAF、STRRED FTW、VsQM、SQI

Previous Work on QoE Prediction Impairments of Videos with Normal Playback Playback interruptions Due to the multiple encoding bitstream representations of the high-quality source content SSIM、 MS-SSIM、 VMAF、 STRRED Due to throughput and buffer limitations FTW、VsQM、 SQI

Available bandwidth LIVE-Netflix dataset .8 different playout patterns(static and dynamic bitrate selection strategies together with playback interruptions)on 14 diverse video contents Gathered approximately 5000 subjective QoE(both continuous and retrospective) scores from 56 subjects,each participating in three 45 minute sessions. sec H.264 compression Playback interruption

LIVE-Netflix dataset H.264 compression Playback interruption • 8 different playout patterns (static and dynamic bitrate selection strategies together with playback interruptions) on 14 diverse video contents • Gathered approximately 5000 subjective QoE (both continuous and retrospective) scores from 56 subjects, each participating in three 45 minute sessions

Is Objective VQA Enough IQA/VOA metric Sa Sall PSNR (IQA.FR) 0.5561 0.5152 PSNRhvs34④IQA,FR) 0.5841 0.5385 SSIM [(IQA,FR) 0.7852 0.7015 MS-SSIM [13](IQA,FR) 0.7532 0.6800 NIQE [35](IQA,NR) 0.3960 0.1697 VMAF [17](VQA,FR) 0.7533 0.6097 STRRED [19(VQA,RR) 0.7996 0.6594 GMSD [36](IQA,FR) 0.6476 0.5812 Sq:videos distorted only by video quality changes with normal playback Sau:all the videos in the dataset

Is Objective VQA Enough ? Sq: videos distorted only by video quality changes with normal playback Sall: all the videos in the dataset

Learning-based Framework for QoE Prediction 1.Objective video quality scores (VQA) Use pooling strategy to collapse per-frame objective quality measurements(i.e.SSIM) 2.Rebuffering-aware features(R1 and R2) Use the length of each rebuffering event measured in seconds(R1)and the number of rebuffering events(R2). 3.Memory-related feature (M) The time since the last rebuffering event or rate drop took place and was completed 4.Impairment duration feature (D)

Learning-based Framework for QoE Prediction 1.Objective video quality scores (VQA) Use pooling strategy to collapse per-frame objective quality measurements (i.e. SSIM) 2.Rebuffering-aware features (R1 and R2) Use the length of each rebuffering event measured in seconds (R1) and the number of rebuffering events (R2). 3. Memory-related feature (M) The time since the last rebuffering event or rate drop took place and was completed 4. Impairment duration feature (I)

Experiments and Results --Regression scheme contributes to better performance (content independence VQA PSNR PSNRhvs3④ SSIM MS-SSIM [13 NIQE [35] VMAF [1Z]STRRED 19] GMSD [36 mean BR 0.6074 0.6252 0.6748 0.6557 0.1391 0.6043 0.6348 0.6496 0.5734 Ridge 0.6687 0.6817 0.7565 0.7461 0.4130 0.6278 0.7957 0.6948 0.6730 Lasso 0.6496 0.6687 0.7461 0.7383 0.4191 0.6409 0.7983 0.6922 0.6691 SVR 0.6313 0.6417 0.8252 0.8226 0.6730 0.6026 0.8704 0.6878 0.7193 ET 0.4265 0.4387 0.8547 0.8752 0.7530 0.4756 0.8439 0.4527 0.6400 RF 0.4931 0.5312 0.8088 0.8154 0.6222 0.4930 0.8104 0.5417 0.6395 GB 0.4830 0.4944 0.7990 0.7899 0.5878 0.5145 0.8032 0.5000 0.6215 VOA PSNR PSNRhvs 34] SSIM MS-SSIM [13] NIQE 35] VMAF [1Z STRRED刃 GMSD 36] mean BR 0.6048 0.6534 0.7288 0.7104 0.3752 0.7561 0.7213 0.6861 0.6545 Ridge 0.8145 0.8224 0.8531 0.8517 0.5984 0.8158 0.8703 0.8254 0.8064 Lasso 0.8192 0.8312 0.8558 0.8514 0.6034 0.8292 0.8719 0.8374 0.8124 SVR 0.7939 0.8016 0.9073 0.8973 0.7633 0.7742 0.9358 0.8106 0.8355 ET 0.6325 0.6392 0.9186 0.9289 0.8407 0.6808 0.9088 0.6869 0.7796 RF 0.6767 0.6922 0.8905 0.8868 0.7182 0.6591 0.8770 0.7026 0.7629 GB 0.6744 0.7060 0.8661 0.8546 0.7143 0.7115 0.8678 0.7043 0.7624 350 8R,5R0CC=-0,6322.LCC=0.7507 RF.SR0CC=0.8832.LCC=0.9139 300 1.0 250 05 200 150 Z 10g -0.50.0 0.5 -050.0 051015

Experiments and Results --Regression scheme contributes to better performance (content independence )

Experiments and Results --Memory-related feature (M)plays an importance role(content independence feature subsets are indexed as follows:VQA(1),M(2),I(3),R+R2(4),VQA+M(5),VQA+I(6).VQA+M+R2(7),M+R1+R2(8). M+H+R1+R2(9),VQA+I+R1+R2(10).VQA+M+R1+R2(11)and VQA+M+I+R1+R2(12). Features 5 6 7 8 9 10 11 12 Ridge 0.6348 0.2296 0.2700 0.3094 0.6000 0.6235 0.7870 0.4105 0.4172 0.78780.7735 0.7957 Lasso 0.6348 0.2296 0.2700 0.3243 0.6304 0.6417 0.7991 0.4075 0.3955 0.8013 0.7991 0.7983 SVR 0.5748 0.3807 0.2758 0.3740 0.7322 0.5878 0.8183 0.4210 0.4839 0.8543 0.8122 0.8704 ET 0.5074 0.3076 0.23450.2993 0.7431 0.5962 0.7496 0.3119 0.3924 0.8348 0.7574 0.8435 RF 0.5304 03961 0.27130.3218 0.7537 0.5691 0.7633 0.4126 0.4656 0.80740.7708 0.8096 GB 0.5691 0.3905 0.2658 0.3527 0.7461 0.6001 0.7668 0.4355 0.4984 0.8070 0.7607 0.8036 Features 6 7 8 10 12 Ridge 0.72130.45070.30490.2930 0.71410.6475 0.7610 0.4602 0.6247 0.78540.7590 0.8703 Lasso 0.7213 0.4507 0.30490.2956 0.7348 0.6956 0.7870 0.4592 0.6201 0.8055 0.7868 0.8719 SVR 0.6454 0.43250.31480.3169 0.8133 0.6472 0.8510 0.4497 0.6959 0.8945 0.8392 0.9358 ET 0.5407 0.3754 0.3110 0.3138 0.7620 0.6031 0.7596 03899 0.6173 0.9004 0.7659 0.9090 RF 0.5685 0.4451 0.35280.32610.7794 0.6024 0.7862 0.4706 0.6966 0.8686 0.7975 0.8742 GB 0.6287 0.4514 0.3514031410.7755 0.6269 0.7904 0.4751 0.7413 0.8665 0.7865 0.8686 Feature importances,ET.NIQE Feature importances,ET.STRRED feature feature

Experiments and Results -- Memory-related feature (M) plays an importance role (content independence )

Experiments and Results --content independence and pattern independence Pattern independence Method SROCC LCC Best PSNR 0.4945 0.5312 PSNR+SQI 32] 0.4989 0.5340 Content independence PSNR+ATLAS 0.4945 0.5321 Ridge SSIM订 0.6615 0.7947 、 Method SROCC LCC Best SSIM+SQI 32] 0.6791 0.7927 FTW 30] 0.3403 0.2956 SSIM+ATLAS 0.7143 0.8650 RF VsQM3面 0.3120 0.2421 MS-SSIM [13] 0.6659 0.7982 PSNR 0.6074 0.6048 MS-SSIM+SQI [321 0.6835 0.7955 SSIM 1] 0.6748 0.7289 MS-SSIM+ATLAS 0.6961 0.8345 GB MS-SSIM [13] 0.6557 0.7104 NIQE 0.4681 0.4107 PSNR+SQI 32] 0.6565 0.6599 NIQE+ATLAS 0.6447 0.6541 RF SSIM+SQI 32] 0.7565 0.8031 VMAF 0.3890 0.4486 MS-SSIM+SQI [321 0.7270 0.7731 VMAF+ATLAS 0.7415 0.7075 RF PSNR+ATLAS 0.6687 0.8145 Ridge STRRED 0.8066 0.7848 SSIM+ATLAS 0.8547 0.9186 ET STRRED+ATLAS 0.8198 0.7923 Ridge MS-SSIM+ATLAS 0.8752 0.9289 ET GMSD 0.4989 0.5545 GMSD+ATLAS 0.5256 0.6679 RF

Experiments and Results -- content independence and pattern independence Content independence Pattern independence

Experiments and Results --generalizability Method SROCC LCC Best Method SROCC LCC Best Method SROCC LCC Best FTW 30] 0.3290 0.3358 VsQM [31 0.2358 0.3324 FTW [30] 0.3352 0.2900 FTW [30] 0.3154 0.3313 PSNR 0.6894 0.6875 VsQM 31] 0.3236 0.2374 VsQM [31] 0.2259 0.3233 SSIM 0.8172 0.8544 PSNR 0.5152 0.5073 PSNR 0.6715 0.6587 MS-SSIM [13] 0.7986 0.8345 SSIM [1] 0.7015 0.7219 SSIM [1] 0.8177 0.8408 SSIMplus [49] 0.8025 0.8414 MS-SSIM [13] 0.6800 0.7104 MS-SSIM [13] 0.7928 0.8168 PSNR+SQI [321 0.7800 0.7535 SSIM+SQI 32] 0.9085 0.9028 PSNR+SQI 321 0.5904 0.5905 PSNR+SQI [32 0.7492 0.7316 MS-SSIM+SQI [32] 0.8891 0.8808 SSIM+SQI [32] 0.7451 0.7070 SSIM+SQI [32] 0.9009 0.8897 SSIMplus+SQI [32] 0.9103 0.9012 MS-SSIM+SQI 32] 0.7239 0.6848 MS-SSIM+SQI [32 0.8807 0.8652 PSNR+ATLAS 0.7799 0.7510 SVR PSNR+ATLAS 0.6155 0.6116 SVR PSNR+ATLAS 0.7439 0.7254 SVR SSIM+ATLAS 0.9142 0.9097 SVR SSIM+ATLAS 0.8203 0.7813 Lasso SSIM+ATLAS 0.9090 0.8963 Lasso MS-SSIM+ATLAS 0.8955 0.8880 Lasso SSIMplus+ATLAS 0.9084 0.8981 Ridge MS-SSIM+ATLAS 0.8000 0.7670 Lasso MS-SSIM+ATLAS 0.8888 0.8716 Lasso Training and Testing for Waterloo training on Waterloo and testing on LIVE-Netflflix training on LIVE-Netflflix and testing on Waterloo

Experiments and Results -- generalizability Training and Testing for Waterloo training on Waterloo and testing on LIVE-Netflflix training on LIVE-Netflflix and testing on Waterloo