A robust fine granularity scalability using trellis-based predictive leak

Abstract

Recently, the MPEG-4 committee has approved the MPEG-4 fine granularity scalability (FGS) profile as a streaming video tool. In this paper, we propose novel techniques to further improve the temporal prediction at the enhancement layer so that coding efficiency is superior to the existing FGS. Our approach utilizes two parameters, the number of bitplanes beta (0 less than or equal to beta less than or equal to maximal number of bitplanes) and the amount of predictive leak alpha (0 less than or equal to alpha less than or equal to 1), to control the construction of the reference frame at the enhancement layer. These parameters alpha and beta can be selected for each frame to provide tradeoffs between coding efficiency and error drift. Our approach offers a general and flexible framework that allows further optimization. It also includes several well-known motion-compensated FGS techniques as special cases with particular sets of alpha and beta. We analyze the theoretical advantages when parameters alpha and beta are used, and provide an adaptive technique to select alpha and beta, which yields an improved performance as compared to that of fixed parameters. An identical technique is applied to the base layer for further improvement. Our experimental results show over 4 dB improvements in coding efficiency using the MPEG-4 testing conditions. The removal of error propagation is demonstrated with several typical channel transmission scenarios.