Two-Dimensional Channel Coding Scheme for MCTF-Based Scalable Video Coding

1. Two-Dimensional Channel Coding Scheme forMCTF-Based Scalable Video Coding IEEE TRANSACTIONS ON MULTIMEDIA,VOL. 9,NO. 1,JANUARY 2007 37 Yu Wang, Student Member, IEEE, Tao Fang, Member, IEEE, Lap-Pui Chau, Senior Member, IEEE, and Kim-Hui Yap, Member, IEEE csk 2007/03/20

2. Outline • Introduction of UEP • Introduction of MCTF • Proposed 2D UEP scheme • Genetic Algorithms • Simulation and Performance • Conclusion

3. Introduction of UEP • Unequal error protection (UEP) is based on the priority encoding transmission (PET) • It has been proven to be very promising to resolve this problem by taking advantage of the differential sensitivities of the output bit-streams of video encoder.

4. Introduction of MCTF • Motion Compensated Temporal Filtering

5. Introduction of MCTF • Motion Compensated Temporal Filtering • Wavelet base • In temporal • Two frame (average , different) • In PSNR • WT with EZW coding

6. Introduction of MCTF ．Reference: Overview on Scalable Video Coding - II

7. Introduction of MCTF • WT with EZW coding

8. Introduction of MCTF

9. Proposed 2D UEP scheme

10. Proposed 2D UEP scheme PSNR increment donated Probability of correctly receiving two-state Markov model approximates

11. Proposed 2D UEP scheme Channel bit-allocation matrix Constraint

12. Genetic Algorithms Genetic Algorithms • Artificial mechanisms of natural evolution • A robust search procedures and solving complex search problems • Disadvantage • Low efficient if large problem space • Population homogeneous

13. Begin Encoding Initialize population Evaluate population Reproduction & Selection Crossover No Mutation Evaluate population Termination criterion End Yes Genetic Algorithms Randomly produce and population size is kept constant Calculate the fitness by PSNR_overall To copy solution strings into a mating pool based on the fitness. Roulette wheel method is used Crossover probability Pc mutation probability Pm

14. Genetic Algorithms • Roulette wheel method

15. Genetic Algorithms (a) A 1 (a) B 1 • Sequence preserving crossover (SPX) • Schemata is preserved as more as possible. 2 9 2 9 3 8 3 8 4 7 4 7 Crossover 5 6 5 6 A=123||5748||69 B=934||5678||21 (a) A’ 1 (a) B’ 1 2 9 2 9 A’=234||5678||91 B’=936||5748||21 3 8 3 8 4 7 4 7 5 6 5 6

16. Genetic Algorithms • Point mutation • Inversion mutation • Shift mutation (a) Point mutation (b) Inversion mutation (c) Shift mutation (right shift)

17. Simulation and Performance • The number of generations: 500 • 300 generations for all the test sequences • l = 100, Pc = 0.65, Pm = 0.02 • All programs were run on an Intel Pentium 4 CPU 3.0G. C language is used for implementation and typically the consumed time for the processing of one group of pictures is about 0.5 s • Groups size 8 • F=4, T=3

18. Simulation and Performance

19. Simulation and Performance

20. Simulation and Performance

21. Conclusion • The MCTF-based SVC can provide flexibly combined temporal, spatial, SNR and complexity scalability. The channel bit allocation for the video with combined scalability in the MCTF based SVC has never been considered. • In this paper, a novel 2-D UEP scheme is proposed for this new technology, which can properly allocate the channel protection bits to the combined temporal and SNR scalable units. • We apply GA to solve the optimization problem. The scheme is compared with other four methods under different channel conditions for a variety of video sequences. The simulation results demonstrate the advantage of our proposed scheme.