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Two-Dimensional Channel Coding Scheme for MCTF-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
csk2007/03/20
Outline
Introduction of UEP Introduction of MCTF Proposed 2D UEP scheme Genetic Algorithms Simulation and Performance Conclusion
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.
Introduction of MCTF
Motion Compensated Temporal Filtering
H
LH
LLL LLH
video sequence
1st temporal level
2nd temporal level
3rd temporal level
H
LH
LLL LLH
video sequence
1st temporal level
2nd temporal level
3rd temporal level
Introduction of MCTF
Motion Compensated Temporal FilteringWavelet base
In temporal Two frame (average , different)
In PSNR WT with EZW coding
Proposed 2D UEP scheme
PSNR increment donated
Probability of correctly receiving
two-state Markov model approximates
Genetic Algorithms
Genetic AlgorithmsArtificial mechanisms of natural evolutionA robust search procedures and solving complex
search problems Disadvantage
Low efficient if large problem spacePopulation homogeneous
Genetic Algorithms
End
Begin
Encoding
Initialize population
Reproduction & Selection
Crossover
Mutation
Evaluate population
Termination criterion
Evaluate population
No
Yes
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
Genetic Algorithms
Sequence preserving crossover (SPX)Schemata is preserved
as more as possible.
A=123||5748||69
B=934||5678||21
A’=234||5678||91
B’=936||5748||21
1
2 9
3 8
4 7
5 6
1
2 9
3 8
4 7
5 6
1
2 9
3 8
4 7
5 6
1
2 9
3 8
4 7
5 6
(a) A
(a) A’
(a) B
(a) B’
Crossover
Point mutation Inversion mutation Shift mutation
Genetic Algorithms
(a) Point mutation
(b) Inversion mutation
(c) Shift mutation (right shift)
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
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.