“POLITEHNICA” UNIVERSITY OF TIMIOARA FACULTY OF ELECTRONICS AND TELECOMMUNICATIONS DEPARTMENT OF COMMUNICATIONS DIPLOMA THESIS VIDEO QUALITY ASSESSMENT IN MOBILE NETWORKS Coordinate, Assoc. Prof. Eng. Dr. Eugen Mârza Graduate, Drago-Florin Iancu Timioara 2010

07/29/2010Drago Iancu2 Contents 1)Introduction 2)Human Visual System (HVS) 3)Mobile Video Streaming Principles 4)Quality Metrics 5)Conclusions

07/29/2010Drago Iancu3 Introduction Topics of interest: vision modeling in the framework of video quality assessment analysis and evaluation of different video quality assessment methods and algorithms over a mobile network determining the one that performs best while correlating well with subjective assessments error detection and concealment (artifacts)

07/29/2010Drago Iancu4 Human Visual System HVS: –very complex –highly adaptive –not equally sensitive to all stimuli –visual information processed on different pathways and channels –color perception based on different spectral sensitivities of photoreceptors => characteristics of the HVS used in the design of vision models and quality metrics

07/29/2010Drago Iancu5 Mobile Video Streaming Principles Video service request in a mobile network –wireless=error prone environment –limited bandwidth means low resolution => loss of 1 packet is a considerable loss of information –real-time => retransmission impossible

07/29/2010Drago Iancu6 Subjective & Objective Quality Assessment

07/29/2010Drago Iancu7 Distortion Artifacts Blocking artifacts Ringing artifacts Clipping Noise Contrast Sharpness Blurriness Jerkiness Mosquito Noise Shimmering Network errors Post-processing errors

07/29/2010Drago Iancu8 Quality Metrics a) MOS (Mean Opinion Score) Subjective test

07/29/2010Drago Iancu9 Quality Metrics b) PSNR (Peak Signal to Noise Ratio)

07/29/2010Drago Iancu10 Quality Metrics c) SSIM (Structural Similarity)

07/29/2010Drago Iancu11 Quality Metrics SSIM simple and multiscale plugins in ImageJ

07/29/2010Drago Iancu12 Conclusions 1. Best results with PSNR metric 2. SSIM best mimics the HVS, however has limitations 3. MOS delivers valuable information for: –optimization –benchmarking 4. methods dependent on: –the correct receival of motion vectors –the presence of scene cuts or fast movement 5. systematic video quality assessment approaches developed to increase flexibility

07/29/2010Drago Iancu13 Thank You!