a robust fine granularity scalability using trellis-based predictive leak hsiang-chun huang, chung-...
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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
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Outline
Introduction Prediction techniques for the
enhancement layer RFGS system architecture Selection of the RFGS parameters Experiment result and analyses Conclusion
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Introduction
base layer and enhancement layer High-quality reference frame Error propagation and drift Balance of coding efficiency and error
robustness
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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
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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
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RFGS system architecture-base layer
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RFGS system architecture-enhancement layer
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RFGS system architecture-generate high quality base layer reference
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Selection of the RFGS parameters
Average weighted difference (AWD)
Use a linear model for computing the near-optimal α
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Selection of the RFGS parameters (cont.)
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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
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Experiment result and analyses
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Experiment result and analyses (cont.)
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Conclusions
Proposed a novel FGS coding technique RFGS
Leaky and partial predictions Achieve a balance between coding
efficiency, error robustness, and bandwidth adaptation