DCT-Prediction Based Progressive Fine Granularity Scalability Coding

In this paper, we propose a novel architecture for scalable video coding, namely, Progressive Fine Granularity Scalable (PFGS) coding, which can provide high coding efficiency along with good bandwidth adaptation and error recovery properties. Unlike the Fine Granularity Scalable (FGS) coding in MPEG-4 proposal, some of the enhancement layers in a current frame are predicted from a high quality enhancement layer in a reference frame, rather than always from the base layer. Using a high quality enhancement layer as the reference makes the motion prediction more accurate to improve the coding efficiency. On the other hand, use of multiple layers of different quality references may also result in increases and fluctuations of the prediction residues to be coded when switching the references, which may limit the coding efficiency improvement. A multiple-layer conditional replenishment approach is used to eliminate this kind of fluctuation. Experimental results show that our coding scheme can improve coding efficiency up to 0.5dB compared with fine granularity scalability coding.