QUALITY OF EXPERIENCE PREDICTION FOR STREAMING VIDEOzduanmu/icme16qoe/paper/... · Video Streaming...

QUALITY OF EXPERIENCE PREDICTIONFOR STREAMING VIDEO

Zhengfang Duanmu

Joint work with Abdul Rehman, Kai Zeng, and Zhou Wang

July 13, 2016

Video Streaming and Quality of ExperienceStreaming Quality Index (SQI)

Conclusions

Outline

1 Video Streaming and Quality of ExperienceVideo StreamingQuality of ExperienceObjective QoE Prediction

2 Streaming Quality Index (SQI)Objective QoE Prediction ModelExperimental Results

3 Conclusions

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Conclusions

Video StreamingQuality of ExperienceObjective QoE Prediction

Outline

3 Conclusions

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Conclusions

The Age of Streaming

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Conclusions

The Age of Streaming

Factors of Picking Streaming ServiceQuality of Experience;

Content;

Price;

Advertisement.

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Conclusions

Quality of Experience

DefinitionThe degree of delight or annoyance of the user of an application orservice. [Callet, 2013]

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Conclusions

Influencing FactorsPlayback smoothness

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Conclusions

Influencing FactorsDuration of initial buffering

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Conclusions

Influencing FactorsVideo presentation quality

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Conclusions

Interaction between Presentation Quality and StallingExperience

0 20 40 60 80 1000

SSIMplus of stalling frames

initial bufferingfitted initial bufferingstallingfitted stalling

Figure: SRCC = 0.79

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Conclusions

Subjective QoE Prediction

Subjective QoE Assessment

Enormous video space

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Conclusions

Existing QoE models: Quality of Service-based

PhilosophyThere exists a causal relationship between generic QoS problems andgeneric QoE problems.

FactorsThroughput -> Delivered video quality

Stalling duration -> Waiting experience

Existing ModelsLinear mapping [Mok, 2011];

Exponential mapping [Hoßfeld, 2012];

Logrithmic mapping [Rodriguez, 2012];

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Conclusions

Existing QoE models: Signal Fidelity-based

PhilosophyQoE can be measured by the distance from test video to the prestinevideo in the video space.

Existing ModelsPSNR;

SSIM [Wang, 2004];

MS-SSIM [Wang, 2003];

VQM [Pinson, 2004];

SSIMplus [Rehman, 2015];

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Conclusions

Our method: Hybrid

MotivationQoS-based: not directly related to human perception;

Signal fidelity-based: only work for static videos;

No modeling on the interaction between video presentation quality andstalling.

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Conclusions

Objective QoE Prediction ModelExperimental Results

Outline

3 Conclusions

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Conclusions

Presentation Quality

Source

Bitrate 1

Bitrate 2

Bitrate 3

Seg1 Seg2 Seg3

Seg3Seg1 Seg2

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Conclusions

0 500 1000 150075

Frame #

Static video qualityStalling position

0 500 1000 150075

Frame # with stallings

PStalling positionRecovery position

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Conclusions

Stalling Experience Quantification

Sk(t) =

Pik−1

(−1 + exp

tf − ikT0

)})ikf ≤ t ≤ ik+lk

Pik−1

(−1 + exp

)})·(

exp{−(

tf − ik − lkT1

)})t > ik+lk

0 otherwise

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Conclusions

Stalling Experience Quantification

Parameters

Parameter DescriptionT0 rate of dissatisfaction in stalling eventT1 strength of memory in stalling event

T init0 rate of dissatisfaction in initial buffering event

T init1 strength of memory in initial buffering eventP0 expectation on initial quality of the video

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Conclusions

0 500 1000 150075

Frame #

Static video qualityStalling position

0 500 1000 150075

PStalling positionRecovery position

0 500 1000 1500−100

S nk )

Stalling positionRecovery position

0 500 1000 15000

QStalling positionRecovery position

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Conclusions

Experimental Results

Performance comparison of QoE models on streaming video QoEdatabase.

PLCC MAE SRCC KRCCFTW [Hoßfeld, 2012] 0.3313 14.9455 0.3154 0.2583

PSNR 0.6663 10.7254 0.6715 0.4697SSIM [Wang, 2004] 0.8432 7.6039 0.8177 0.6070

SSIMplus [Rehman, 2015] 0.8350 7.6934 0.8024 0.5924SQI-PSNR 0.7391 9.8445 0.7492 0.5434SQI-SSIM 0.9015 5.8941 0.9009 0.7238

SQI-SSIMplus 0.9026 5.8330 0.9007 0.7213

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Conclusions

Experimental Results

40 60 80 1000

no stallinginitial bufferingstalling

0.85 0.9 0.95 10

20 40 60 80 1000

SSIMplus

0.5 0.6 0.7 0.8 0.9 10

0.8 0.85 0.9 0.95 10

SQI−SSIM

0 20 40 60 80 1000

SQI−SSIMplusM

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Conclusions

Outline

3 Conclusions

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Conclusions

ContributionProposed an objective QoE model for video streaming that considerspresentation quality and its interaction with stalling;

Achieved the best performance in predicting subject opinions.

Future WorkConstruct comprehensive database;

Investigate other QoE-related factors;

Improve the QoE model.

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Conclusions

References

P. L. Callet, S. MÃuller, and A. Perkis “Qualinet White Paper on Definitions of Quality ofExperience”, European Network on Quality of Experience in Multimedia Systems and Services,2013.

R. Mok, E. Chan, and R. Chang, “Measureing the quality of experience of HTTP video streaming,”IFIP/IEEE Int. Sym. Integrated Network Management, 2011.

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, 2012.

D. Rodriguez, J. Abrahao, D. Begazo, R. Rosa, G. Bressan, “Quality metric to assess videostreaming service over TCP considering temporal location of pauses,” IEEE TCE, 2012.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From errorvisibility to structural similarity,” IEEE TIP, 2004.

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image qualityassessment,” IEEE Asilomar, 2003.

M. H. Pinson, S. Wolf, “A new standardized method for objectively measuring video quality,” IEEETrans. Broadcasting, 2004.

A. Rehman, K. Zeng, and Z. Wang, “Display device-adapted video quality-of-experienceassessment,” SPIE Electronic Imaging, 2015.

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Conclusions

Thank you

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QUALITY OF EXPERIENCE PREDICTION FOR STREAMING VIDEOzduanmu/icme16qoe/paper/... · Video Streaming...

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