Fine-Granular Motion Matching for Inter-View Motion Skip Mode in Multiview Video Coding

As motion of neighboring views is highly correlated in multiview video systems, the inter-view motion skip mode has been proposed to improve the efficiency of multiview video coding (MVC) by reusing the motion information of neighboring views. To fully exploit the inter-view motion correlation, a fine-granular motion matching algorithm for the motion skip mode is presented in this paper. The proposed algorithm searches for the best matching motion information in neighboring views with fine granularity, and then uses the motion information for the motion-compensated coding in the motion skip mode. The rate-distortion optimization criterion is applied to find the best matching motion information. Experimental results show that the proposed algorithm can improve the performance of the motion skip mode, and further improve the efficiency of MVC.