Error Resilience in a Generic Compressed Video Stream Transmitted over a Wireless Channel

1. Error Resilience in a Generic Compressed Video Stream Transmitted over a Wireless Channel Muhammad Bilal 2005-06-0020

2. Channel Noise • Markov Process Model • AWGN Model • Transfer Function Model • Hata Model, Akumura Model

3. Error Correction Methods • Redundancy • Header information • Motion Vectors • DC coefficient • Source Coding • Reed Solomon • Hamming Code • Channel Equalization • Channel coding • Channel response

4. Error Localization • Reversible Variable Length Codes • Fixed Synchronization Markers

5. Error Resilience Methods • Data Partitioning • SNR scalability • Prediction • Intra coded frame • Copy previous block • DC coefficient prediction • Inter coded frame • Motion Vector Data based prediction

6. Framework • A generic video compressor • MATLAB implementation • ‘MPEG-2 like’ bit stream • Platform for video coding analysis • Compression efficiency • Motion Estimation (offset distortion) • Data Partitioning • Etc • Demonstration of good quality & highly quantized videos

7. Error Introduction Methods • Arbitrary bursts of error in bit stream • Header loss • RVLC synchronization problem • Need to deal with resynchronization

8. Error Introduction Methods (contd.) • Intelligent error introduction (Macroblock level) • Assume bit stream remains synchronized • Error in coefficients/motion vector data • SNR degradation • Demonstration • Error propagation due to motion compensation • Need for ‘I’ frame GDR (Gradual Data Refresh)

9. Quality Measures • Subjective evaluation • SNR • Deceiving results for some sequences

10. Analysis • Effect of various error concealment methods • I Frames • DC Coefficients saved • ‘D’ frame • DC Coefficients not saved • Copy previous frame block • Error propagation due to motion compensation

11. Analysis (contd.) • ‘P’ Frames • Dependent on ‘I’ frame (error propagation) • Dependent on content • High motion content (Foreman) • Head & Shoulder (News) • Camera panning (Coastguard) • Motion Vector Data + DCT • DCT data useless without MV • MV data useful without DCT data • demonstration

12. Analysis (contd.) • ‘P’ Frames • Absence of DCT data • Copy motion compensated block • Previous frame non MC block degrades video for high motion content

13. Analysis (contd.) • ‘P’ Frames • Dependency on ‘I’ frames • Perfect ‘I’ frame decoding • DCT data destroyed • MV data available • Demonstration • Seamless video (news) • Acceptable video for many purposes (coastguard, foreman)

14. Analysis (contd.) • Critical Data • I Frame • DC coefficients • P Frame • I frame • MV data • Motion Estimation algorithm • Attempt to find the ‘actual’ motion vector

15. SNR vs BER

16. SNR vs BER

17. SNR vs BER

18. SNR vs BER

19. Conclusion • Error Resilience • Error localization • Parity • Hamming Codes • Redundancy • DC coefficients + MV data • I frame perfect decoding (BW demanding) • Compensate with more number of P frames in GOP

20. Conclusion (contd.) • Data Partitioning • Critical data positioned close to resynchronization marker • DC coefficients in ‘I’ frames • MV data in ‘P’ frames

21. Conclusion (contd.) • Further compression! • Randomly introduce ‘not coded’ blocks • Depend on decoder error concealment scheme • Infrequent ‘not coded’ blocks will result in seamless video decoding

22. Q&A