Effective HEVC intra coding unit size decision based on online progressive Bayesian classification
Jiawei Chen, Lu Yu · 2016
In High Efficiency Video Coding (HEVC), optimal coding unit (CU) size is decided based on recursive rate-distortion cost comparison, which consumes high computational resources. In this paper, an effective HEVC intra CU size decision algorithm is proposed to speed up the encoding process. The algorithm is based on progressive Bayesian classification, which is composed of two cascade classifiers: a three-class classifier and a binary classifier, at every coding depth. The three-class classifier firstly partitions the feature space into three regions: split, indistinct and non-split regions. Then, the binary classifier further partitions the indistinct region into split and non-split regions by utilizing additional complicated features. The thresholds of the two classifiers are well designed based on Bayesian risk to balance coding efficiency and complexity. All classifiers are online trained and experimental results show that the proposed algorithm can save approximately 51%~63% of the total encoding time of HM 15.0 with negligible loss on rate-distortion performance.