An efficient skip mode competition scheme based on vector clustering and object boundary detection

In video coding, a portion of skipped macroblocks is highly effective to the coding performance, especially, at low bitrates since, for a skip mode, no additional coding parameters are transmitted except mode index itself. To increase the skip mode occurrence, in this paper, we propose an efficient skip mode competition scheme based on vector clustering of multiple predictive motion vectors (PMV) and boundary detection of moving objects. The proposed scheme selects the best PMV of skip mode optimally among representative PMV candidates which are obtained after the clustering process and switches a more appropriate competition scheme adaptively according to object boundary conditions. Experimental results show that the proposed scheme reduces average 4.31% in BDRATE compared to the KTA 2.6 software.