Stack robust fine granularity scalability

A scalable video coding technique, named as stack robust fine granularity scalability (SRFGS), is presented to provide simultaneously temporal scalability and SNR scalability. SRFGS first simplifies the RFGS as stated in H. C. Huang et al. (2002) temporal prediction architecture and then generalizes the prediction concept as following: the quantization error of the previous layer can be inter-predicted by the reconstructed frame in the previous time instance of the same layer. With this concept, the RFGS architecture can be extended to multiple layers, which form the stack architecture, SRFGS can be optimized at several operating points to fit the requirement of various applications, while still maintaining the fine granularity and error robustness of RFGS. Thus, the stack prediction of SRFGS can improve the temporal prediction efficiency of RFGS. The simulation results show that SRFGS can improve the performance of RFGS by 0.5 to 3.0 dB in PSNR. In addition, SRFGS has been submitted to MPEG committee according to H.C. Huang et al. (2003) and ranked as one of the best algorithms according to the subjective testing in the Report on Call for Evidence on Scalable Video Coding (2003).