Stack robust fine granularity scalable video coding

Abstract

A novel scalable video coding technique, namely Stack Robust Fine Granularity Scalability (SRFGS), is presented to provide both temporal and SNR scalability. The SRFGS first simplifies the temporal prediction architecture of RFGS. The approach is further generalized using a reconstructed frame from the previous time instance of the same layer to temporally predict the quantization error of the lower layer. With this concept, the RFGS architecture can be extended to multi-layer stack architecture. The SRFGS can be optimized at several operating points to meet the requirements of various applications, while maintaining the fine granularity and error robustness of RFGS. An optimized macroblock-based alpha adaptation scheme is proposed to improve the coding efficiency. A single-loop enhancement layer decoding scheme is proposed to reduce the decoder complexity. The simulation results show that SRFGS can improve the performance of RFGS by 0.4 to 3.0 dB in PSNR. SRFGS has been reviewed by the MPEG committee and ranked as one of the best algorithms according to subjective testing in the Report on Call for Evidence on Scalable Video Coding.