A framework for efficient progressive fine granularity scalable video coding

In this paper, a basic framework for efficient scalable video coding, namely progressive fine granularity scalable (PFGS) video coding is proposed. Similar to the fine granularity scalable (FGS) video coding in MPEG-4, the PFGS framework has all the features of FGS, such as fine granularity bit-rate scalability, channel adaptation, and error recovery. On the other hand, different from the FGS coding, the PEGS framework uses multiple layers of references with increasing quality to make motion prediction more accurate for improved video-coding efficiency. However, using multiple layers of references with different quality also introduces several issues. First, extra frame buffers are needed for storing the multiple reconstructed reference layers. This would increase the memory cost and computational complexity of the PEGS scheme. Based on the basic framework, a simplified and efficient PEGS framework is further proposed. The simplified PFGS framework needs only one extra frame buffer with almost the same coding efficiency as in the original framework. Second, there might be undesirable increase and fluctuation of the coefficients to be coded when switching from a low-quality reference to a high-quality one, which could partially offset the advantage of using a high-quality reference. A further improved PFGS scheme can eliminate the fluctuation of enhancement-layer coefficients when snitching references by always using only one high-quality prediction reference for all enhancement layers. Experimental results show that the PFGS framework can improve the coding efficiency up to more than 1 dB over the FGS scheme in terms of average PSNR, yet still keeps all the original properties, such as fine granularity, bandwidth adaptation, and error recovery. A simple simulation of transmitting the PFGS video over a wireless channel further confirms the error robustness of the PEGS scheme, although the advantages of PFGS have not been fully exploited.