Motion vector prediction in multiview video coding

Abstract

In video coding, motion vectors always account for a large number of bits and affect coding efficiency largely. In this paper, we propose an efficient motion vector prediction algorithm for multiview video coding (MVC), which can predict motion vectors from adjacent views and achieve good prediction accuracy. We first investigate the correlations among different views and describe the disparity between adjacent views as global motion. The affine model is used to compute the global parameters between frames of adjacent views. At least one view is coded independently without interview prediction. After that, motion vectors of the frame to be coded can be derived from the motion vectors of the co-located coded frame in adjacent view using the global motion information. A rate-distortion optimization scheme is used to choose between the proposed method and traditional motion compensated prediction method. Experimental results show that, compared to simulcast coding, the proposed algorithm can achieve good performance and improve the coding efficiency up to 0.8 dB in PSNR.