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, rebufferingaware 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