a robust fine granularity scalability using trellis-based predictive leak hsiang-chun huang, chung-...

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