Early SKIP mode decision based on Bayesian model for HEVC

In High Efficiency Video Coding (HEVC), SKIP mode is an efficient inter prediction tool with high coding performance but low complexity. In this paper, an early SKIP mode decision algorithm is proposed to accelerate the encoding process. The rate-distortion cost (RD-cost) of SKIP mode is used as the decision criterion. A nonparametric density estimation scheme is employed to partition the SKIP RD-cost distribution space into high distinction region (HDR) and low distinction region (LDR). For a given Coding Unit (CU), if the SKIP RD-cost falls into the HDR, the SKIP mode is directly selected as the optimal mode. If the RD-cost maps into LDR, a Bayes risk minimization rule is adopted to ensure RD performance. The statistical parameters are updated according to different QPs and CU depths. Experimental results show that the proposed algorithm can reduce 47% of encoding time with only 0.34% of BD-bitrate increase on average.