QUALITY OF EXPERIENCE PREDICTION FOR STREAMING VIDEO. Zhengfang Duanmu

Transcription

1 QUALITY OF EXPERIENCE PREDICTION FOR STREAMING VIDEO Zhengfang Duanmu Joint work with Abdul Rehman, Kai Zeng, and Zhou Wang July 13, 2016

2 2 / 26 Outline and 1 and 2 Model 3

3 3 / 26 Outline and 1 and 2 Model 3

4 and The Age of Streaming 4 / 26

5 and The Age of Streaming Factors of Picking Streaming Service ; Content; Price; Advertisement. 5 / 26

6 and Definition The degree of delight or annoyance of the user of an application or service. [Callet, 2013] 6 / 26

7 and Influencing Factors Playback smoothness 7 / 26

8 and Influencing Factors Duration of initial buffering 8 / 26

9 and Influencing Factors Video presentation quality 9 / 26

10 10 / 26 and Interaction between Presentation Quality and Stalling Experience initial buffering fitted initial buffering stalling fitted stalling MOS drop SSIMplus of stalling frames Figure: SRCC = 0.79

11 and Subjective QoE Prediction Subjective QoE Assessment Enormous video space 11 / 26

12 and Existing QoE models: Quality of Service-based Philosophy There exists a causal relationship between generic QoS problems and generic QoE problems. Factors Throughput -> Delivered video quality Stalling duration -> Waiting experience Existing Models Linear mapping [Mok, 2011]; Exponential mapping [Hoßfeld, 2012]; Logrithmic mapping [Rodriguez, 2012]; 12 / 26

13 and Existing QoE models: Signal Fidelity-based Philosophy QoE can be measured by the distance from test video to the prestine video in the video space. Existing Models PSNR; SSIM [Wang, 2004]; MS-SSIM [Wang, 2003]; VQM [Pinson, 2004]; SSIMplus [Rehman, 2015]; 13 / 26

14 and Our method: Hybrid Motivation QoS-based: not directly related to human perception; Signal fidelity-based: only work for static videos; No modeling on the interaction between video presentation quality and stalling. 14 / 26

15 15 / 26 Outline and Model 1 and 2 Model 3

16 and Presentation Quality Model Source T R A N S C O D I N G Bitrate 1 Bitrate 2 Bitrate 3 V Q A S E G M E N T E R Seg1 Seg2 Seg3 Seg1 Seg2 Seg3 Seg1 Seg2 Seg3 16 / 26

17 17 / 26 and Presentation Quality Model Static video quality Static video quality Stalling position Streaming video quality (P) P Stalling position Recovery position Frame # Frame # with stallings

18 18 / 26 and Stalling Experience Quantification Model Stalling Experience Quantification ( { ( )}) tf ik i P ik exp k T f t i k+l k f ( { ( )}) 0 lk S k P (t) = ik exp ( { ( T)}) 0 tf ik l k exp t > i k+l k T f 1 0 otherwise (1)

19 19 / 26 and Stalling Experience Quantification Model Parameters Parameter Description T 0 rate of dissatisfaction in stalling event T 1 strength of memory in stalling event T0 init rate of dissatisfaction in initial buffering event T1 init strength of memory in initial buffering event expectation on initial quality of the video P 0

20 20 / 26 and Presentation Quality Model Static video quality Static video quality Stalling position Streaming video quality (P) P Stalling position Recovery position Frame # Frame # with stallings Stalling experience (S n k ) S n 80 2 S n Stalling position Recovery position Frame # with stallings QoE (Q) Q Stalling position Recovery position Frame # with stallings

21 and Model Performance comparison of QoE models on streaming video QoE database. PLCC MAE SRCC KRCC FTW [Hoßfeld, 2012] PSNR SSIM [Wang, 2004] SSIMplus [Rehman, 2015] SQI-PSNR SQI-SSIM SQI-SSIMplus / 26

22 22 / 26 and Model no stalling initial buffering stalling no stalling initial buffering stalling MOS MOS MOS no stalling initial buffering stalling FTW SSIM SSIMplus no stalling initial buffering stalling no stalling initial buffering stalling MOS MOS MOS no stalling initial buffering stalling VsQM SQI SSIM SQI SSIMplus

23 23 / 26 Outline and 1 and 2 Model 3

24 and 24 / 26 Contribution Proposed an objective QoE model for video streaming that considers presentation quality and its interaction with stalling; Achieved the best performance in predicting subject opinions. Future Work Construct comprehensive database; Investigate other QoE-related factors; Improve the QoE model.

25 and References 25 / 26 P. L. Callet, S. MÃűller, and A. Perkis Qualinet White Paper on Definitions of Quality of Experience, European Network on in Multimedia Systems and Services, R. Mok, E. Chan, and R. Chang, Measureing the quality of experience of HTTP video streaming, IFIP/IEEE Int. Sym. Integrated Network Management, T. Hoßfeld, S. Egger, R. Schatz, K. Masuch, and C. Lorentzen, Initial delay vs. interruption: between the devil and the deep blue sea, IEEE QoMEX, D. Rodriguez, J. Abrahao, D. Begazo, R. Rosa, G. Bressan, Quality metric to assess video streaming service over TCP considering temporal location of pauses, IEEE TCE, Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE TIP, Z. Wang, E. P. Simoncelli, and A. C. Bovik, Multiscale structural similarity for image quality assessment, IEEE Asilomar, M. H. Pinson, S. Wolf, A new standardized method for objectively measuring video quality, IEEE Trans. Broadcasting, A. Rehman, K. Zeng, and Z. Wang, Display device-adapted video quality-of-experience assessment, SPIE Electronic Imaging, 2015.

26 and 26 / 26 Thank you