Fast HEVC Coding Unit Decision Based On BP-Neural Network

Abstract

High Efficiency Video Coding (HEVC) employs a highly flexible quad-tree coding block partitioning structure characterized by a coding unit (CU), prediction unit (PU) and transform unit (TU). Taking the computational complexity of CU splitting in HEVC into consideration, a novel fast CU selection algorithm based on a back-propagation neural network (BPNN) is proposed in this paper. To find the best prediction mode at the current CU, the BPNN classifier is designed for two kinds of outputs representing “split” and “unsplit”. Thus, whether to split the current CU or not is directly judged by the outputs of the BPNN classifier. With this new scheme, the unnecessary CU splitting process can be skipped in advance. Experiments and results show that the proposed algorithm could greatly reduce the high computational complexity and the average time savings reaches 47.75% in Random Access Main configuration and 41.94% in Low Delay B Main configuration with a BD-bit rate increase of 1.89%.