Model-based optimal dependent joint bit allocation of H.264/AVC statistical multiplexing
In this paper, we address the dependent joint bit allocation problem in H.264/AVC statistical multiplexing. For most of the existing methods, in order to improve the overall visual quality, the bit allocation is performed upon the relative complexities of different video programs. However, due to the temporal prediction employed in H.264, the influence of current frame to the rate-distortion (R-D) performances of the future frames should be taken in to account as well. The contributions of the paper are two-fold. First, a simple but accurate inter-frame dependency model (IFDM) is introduced which can quantitatively measure the coding dependency between the current coding frame and its reference frame. Second, based on the IFDM, the dependent joint bit allocation problem is revisited, and both the frame complexity and inter-frame dependency are considered in the bit allocation process. Then, it is proved that the dependent joint bit allocation problem can actually be relaxed into a convex optimization problem which can be optimally and efficiently solved. Experimental results demonstrate that the proposed dependent bit allocation method achieves 33.45% and 11.10% bitrate reduction on average compared with the equivalent bit allocation (EBA) and the optimal independent joint bit allocation (OIJBA) methods respectively.