Fast CU partition-based machine learning approach for reducing HEVC complexity

Abstract

With the development of video coding technology, the high efficiency video coding (HEVC) provides better coding efficiency compared to its predecessors H.264/AVC. HEVC improves rate distortion (RD) performance significantly with increased encoding complexity. Due to the adoption of a large variety of coding unit (CU) sizes, at RD optimization level, the quadtree partition of the CU consumes a large proportion of the encoding complexity. Hence, the computational complexity cost remains a critical issue that must be properly considered in the optimization task. In this paper, two machine learning-based fast CU partition method for inter-mode HEVC are proposed, to optimize the complexity allocation at CU level. First, we propose an online support vector machine (SVM)-based fast CU algorithm for reducing HEVC complexity. The later was trained in an online way. Second, a deep convolutional neural network (CNN) is designed to predict the CU partition, in which large-scale training database including substantial CU partition data is considered. Experimental results demonstrate that the proposed online SVM can achieve a time saving of 52.28% with a degradation of 1.928% in the bitrate (BR). However, the proposed deep CNN can reduce the encoding time by 53.99% with 0.195% BR degradation. Compared to the state-of-the art, the two proposed approaches outperform the related works in terms of both RD performance and complexity reduction at inter-mode.