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fast CU size decision algorithm using machine learning for HEVC intra coding

SIGNAL PROCESSING-IMAGE COMMUNICATION 2018年 62卷 33-41页

作者： Lee, Dokyung Jeong, Jechang Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea

High Efficiency Video coding (HEVC) is a state-of-the-art video compression standard which improves coding efficiency significantly compared with the previous coding standard, H.264/AVC. In the HEVC standard, novel technologies consuming massive computational power are adopted, such as quad-tree-based coding unit (CU) partitioning. Although an HEVC encoder can efficiently compress various video sequences, the computational complexity of an exhaustive search has become a critical problem in HEVC encoder implementation. In this paper, we propose a fast algorithm for the CU partitioning process of the HEVC encoder using machine learning methods. A complexity measure based on the Sobel operator and rate-distortion costs are defined as features for our algorithm. A CU size can be determined early by employing Fisher's linear discriminant analysis and the k-nearest neighbors classifier. The statistical data used for the proposed algorithm is updated by adaptive online learning phase. The experimental results show that the proposed algorithm can reduce encoding time by approximately 54.0% with a 0.68% Bjontegaard-Delta bit-rate increase. (C) 2017 Elsevier B.V. All rights reserved.

关键词： HEVC fast coding unit size decision Fisher's linear discriminant analysis k-nearest neighbors classifier

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fast intra coding unit decision for high efficiency video coding based on statistical information

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2017年 55卷 121-129页

作者： Lee, Dokyung Jeong, Jechang Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea

The latest video coding compression standard is known as high efficiency video coding (HEVC). It supports high resolution video sequences and has better coding performance than the,previous standard H.264/AVC. A quad tree based coding unit (CU) partitioning process is one of the most efficient technologies used in an HEVC encoder. A coding tree unit (typically 64 x 64) can be split into smaller CUs based on rate-distortion optimization, allowing various types of video content to be adaptively compressed. In addition, intra prediction of the HEVC standard supports 35 prediction modes (planar, DC, and 33 angular modes) to improve coding efficiency. However, the computational complexity of HEVC encoder becomes a critical problem when implement with an encoder. Thus, a fast CU size decision algorithm for intra prediction of an HEVC encoder is proposed in this study. We utilize image complexity and an adaptive depth prediction for early split CU decision making. In addition, the Bayesian decision rule and quadratic discriminant analysis are used for early termination of the CU partitioning process. Experimental results show that our proposed algorithm considerably reduces encoding time by approximately 55.47% with only a small BD-BR loss (1.01%) compared to the HEVC reference software HM 16.0.

关键词： High-efficiency video coding (HEVC) fast coding unit size decision Online learning algorithm Bayes decision Quadratic discriminant analysis (QDA) Video compression

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