Fast CU Partitioning Algorithm for HEVC Using an Online-Learning-Based Bayesian Decision Rule

Abstract

High Efficiency Video Coding (HEVC) is the state-of-the-art video coding standard. It adopts a hierarchical quad-tree-based coding unit (CU) partitioning structure that is flexible in various texture and motion characteristics of a video signal. However, the exhaustive partitioning process for finding optimal CU partitions requires a dramatic increase in computational complexity of the HEVC encoder compared with previous video coding standards. In this paper, a fast CU partitioning algorithm is proposed for HEVC encoder, which early on terminates the CU partitioning process based on the Bayesian decision rule using joint online and offline learning. An online learning method is first presented based on the minimum error Bayesian decision rule using a training picture selection method with scene change detection. Next, a joint online and offline learning method is presented, which additionally trains the loss of decision making of the proposed method based on the minimum risk Bayesian decision rule. The proposed method is implemented on an HEVC test software 15.0. Experimental results show that the proposed method reduces the computational complexity of HEVC encoder to 53.6% on an average with a 0.71% acceptable Bjøntegaard delta bitrate loss in random access configuration. For other configurations, 48.4%, 48.5%, and 54.2% encoding time saving are obtained on an average for low delay, low delay-P, and all intra-configurations, respectively.