Fast Bayesian decision based block partitioning algorithm for HEVC

The newly published High Efficiency Video Coding (HEVC) Standard has greatly enhanced the coding performance in comparison to its predecessors. However, HEVC also has high computational complexity, which limits its application. In this paper, we propose a fast Bayesian Decision based Block Partitioning (BDBP) algorithm for HEVC encoder. Firstly, the scene change detection based on average grey difference is used to divide the video sequence into the online learning phase and the fast partitioning phase. Secondly, in the online learning phase, the statistical parameters are extracted from coding units (CUs) in every depth to establish the Gaussian mixture models which are resolved by expectation maximization algorithm; in the fast partitioning phase, the conditional probabilities for CU to decide partitioning and non-partitioning are calculated. Finally, the minimum risk Bayesian decision rule is used to choose the decision with smaller risk, and the decision is regarded as the judgment of the current CU. Experimental results show that the proposed algorithm reduces the computational complexity of HM13.0 to 54.1% in encoding time with 0.92% increase in the BD-Rate and 0.05dB decrease in the BD-PSNR. Moreover, the proposed algorithm also demonstrates better performance over other state-of-the-art work.