Rate-distortion analysis of leaky prediction based FGS video for constant quality constrained rate adaptation

For leaky prediction based FGS (Fine Granularity Scalability), constant quality constrained rate adaptation, i.e., how to optimally truncate/allocate bits given the current channel bandwidth, is still an open problem. The difficulty lies in obtaining accurate R-D (rate-distortion) curves for leaky prediction based FGS (L-FGS) due to the dependency among video frames. In this paper, we propose an accurate R-D model, which considers not only the distortion introduced in the current frame and the propagated distortion from the reference frame due to rate adaptation, but also the correlation between them. An excellent property of our proposed R-D model is that even when applying the model for a long video sequence without any update of the actual distortion values, the estimation error is still negligible and the error is not accumulated. Based on our proposed R-D model, a sliding window technique is further developed to solve the problem of constant quality constrained bit allocation. Experimental results show that the proposed R-D model is very accurate and the corresponding bit allocation algorithm can achieve much more smooth video quality than the traditional uniform bit allocation under both CBR (constant bit rate) and VBR (variable bit rate) channels.