AN ADAPTIVE CU MODE DECISION MECHANISM BASED ON BAYESIAN DECISION THEORY FOR H.265/HEVC

H.265/HEVC aims to provide significant improvement of compression performance compared with previous coding standards at cost of significant computation complexity. In this paper, an adaptive CU mode decision mechanism (ACMD) based on Bayesian decision theory is proposed to accelerate mode selection procedure. Specifically, the homogeneous determination is firstly utilized to filter out non-split LCU and then the feature space related to CU mode decision is introduced, which is divided into two regions according to Bayesian risk. Bayesian classifier is employed in low-risk region while in high-risk region, the mode decision is made by rate distortion cost. Experimental results demonstrate that the proposed algorithm provides averagely 34.28% encoding time reduction while maintaining the same level of perceptual visual quality, compared with HEVC test mode (HM 10.0) encoder with low-delay configurations.