Dependent Joint Bit Allocation for H.264/AVC Statistical Multiplexing Using Convex Relaxation

In this paper, we address the dependent joint bit allocation problem in H.264/AVC statistical multiplexing. In most existing methods, to improve the overall visual quality, the bit allocation is based upon the instantaneous relative frame complexity of different video programs. However, due to the temporal prediction employed in H.264, the influence of a frame on the rate-distortion characteristics of the future frames should be taken into account as well. In this paper, we use our previously proposed simple, but accurate, inter-frame dependency model (IFDM) to quantitatively measure the coding dependency between the current frame and its reference frame. Based on the IFDM, we formulate the dependent joint bit allocation problem, considering both the inter-program relative frame complexity and intra-program coding dependency. We prove that the dependent joint bit allocation (DeJoBA) problem can actually be formulated and relaxed into a convex optimization problem, which can be optimally and efficiently solved. Experimental results suggest that the proposed DeJoBA method can achieve 36.81% and 13.11% bitrate reduction, on average, compared with the equal bit allocation and optimal independent joint bit allocation methods, respectively.