a robust fine granularity scalability using trellis-based predictive leak hsiang-chun huang, chung-...
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TRANSCRIPT
A Robust Fine Granularity Scalability Using Trellis-Based Predictive Leak
Hsiang-Chun Huang, Chung-Neng Wang and Tihao Chiang
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 12, NO. 6, JUNE 2002
Outline
Introduction Prediction techniques for the
enhancement layer RFGS system architecture Selection of the RFGS parameters Experiment result and analyses Conclusion
Introduction
base layer and enhancement layer High-quality reference frame Error propagation and drift Balance of coding efficiency and error
robustness
Prediction techniques for the enhancement layer
MPEG-4 FGS : the best error robustness
SNR scalable approach : the best coding efficiency
Robust FGS(RFGS) : strike a balance between these two approach
Prediction techniques for the enhancement layer (cont.)
Two MC prediction techniques : Leaky Prediction : 0α1
used to speed up the decay of error energy in the temporal directions
Partial Prediction : 0βmaximal number of bitplanes1.βincreased, improved coding efficiency
2.βbitplanes is lost, the error will be attenuated by αtimes for each frame at the enhancement layer
RFGS system architecture-base layer
RFGS system architecture-enhancement layer
RFGS system architecture-generate high quality base layer reference
Selection of the RFGS parameters
Average weighted difference (AWD)
Use a linear model for computing the near-optimal α
Selection of the RFGS parameters (cont.)
Selection of the RFGS parameters (cont.)
Performance is better when 2-4 bitplanes are used for coding
Identical β is better than distinct β β = 2 when bandwidth512K β = 3 when bandwidth1.2M β = 4 when bandwidth is even higher
Experiment result and analyses
Experiment result and analyses (cont.)
Conclusions
Proposed a novel FGS coding technique RFGS
Leaky and partial predictions Achieve a balance between coding
efficiency, error robustness, and bandwidth adaptation