“P OLITEHNICA ” U NIVERSITY OF T IMIȘOARA FACULTY OF ELECTRONICS AND TELECOMMUNICATIONS DEPARTMENT OF COMMUNICATIONS

1. “POLITEHNICA” UNIVERSITYOF TIMIȘOARAFACULTY OF ELECTRONICS AND TELECOMMUNICATIONSDEPARTMENT OF COMMUNICATIONS DIPLOMA THESIS VIDEO QUALITY ASSESSMENT IN MOBILE NETWORKS Coordinate, Assoc. Prof. Eng. Dr. Eugen Mârza Graduate, Dragoș-Florin Iancu Timișoara 2010

2. Contents • Introduction • Human Visual System (HVS) • Mobile Video Streaming Principles • Quality Metrics • Conclusions Dragoș Iancu

3. 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) Dragoș Iancu

4. 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 Dragoș Iancu

5. 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 Dragoș Iancu

6. Subjective & Objective Quality Assessment Dragoș Iancu

7. Distortion Artifacts • Blocking artifacts • Ringing artifacts • Clipping • Noise • Contrast • Sharpness • Blurriness • Jerkiness • Mosquito Noise • Shimmering • Network errors • Post-processing errors Dragoș Iancu

8. Quality Metrics a) MOS (Mean Opinion Score) Subjective test Dragoș Iancu

9. Quality Metrics b) PSNR (Peak Signal to Noise Ratio) Dragoș Iancu

10. Quality Metrics c) SSIM (Structural Similarity) Dragoș Iancu

11. Quality Metrics • SSIM simple and multiscale plugins in ImageJ Dragoș Iancu

12. 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 Dragoș Iancu

13. Thank You! Dragoș Iancu