Machine Learning-Based Coding Unit Depth Decisions for Flexible Complexity Allocation in High Efficiency Video Coding
文献类型：期刊论文
| 作者 | Zhang, Yun1; Kwong, Sam2,3; Wang, Xu4; Yuan, Hui5; Pan, Zhaoqing6; Xu, Long7
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2015-07-01 |
| 卷号 | 24期号:7页码:2225-2238 |
| 关键词 | High efficiency video coding coding unit machine learning support vector machine |
| 英文摘要 | In this paper, we propose a machine learning-based fast coding unit (CU) depth decision method for High Efficiency Video Coding (HEVC), which optimizes the complexity allocation at CU level with given rate-distortion (RD) cost constraints. First, we analyze quad-tree CU depth decision process in HEVC and model it as a three-level of hierarchical binary decision problem. Second, a flexible CU depth decision structure is presented, which allows the performances of each CU depth decision be smoothly transferred between the coding complexity and RD performance. Then, a three-output joint classifier consists of multiple binary classifiers with different parameters is designed to control the risk of false prediction. Finally, a sophisticated RD-complexity model is derived to determine the optimal parameters for the joint classifier, which is capable of minimizing the complexity in each CU depth at given RD degradation constraints. Comparative experiments over various sequences show that the proposed CU depth decision algorithm can reduce the computational complexity from 28.82% to 70.93%, and 51.45% on average when compared with the original HEVC test model. The Bjontegaard delta peak signal-to-noise ratio and Bjontegaard delta bit rate are -0.061 dB and 1.98% on average, which is negligible. The overall performance of the proposed algorithm outperforms those of the state-of-the-art schemes. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000353142300003 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/5762]  |
| 专题 | 国家天文台_太阳物理研究部 |
| 作者单位 | 1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China 2.City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China 3.City Univ Hong Kong, Shenzhen Res Inst, Hong Kong, Hong Kong, Peoples R China 4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 5.Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China 6.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Jiangsu Engn Ctr Network Monitoring, Nanjing 210044, Jiangsu, Peoples R China 7.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 130117, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yun,Kwong, Sam,Wang, Xu,et al. Machine Learning-Based Coding Unit Depth Decisions for Flexible Complexity Allocation in High Efficiency Video Coding[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(7):2225-2238. |
| APA | Zhang, Yun,Kwong, Sam,Wang, Xu,Yuan, Hui,Pan, Zhaoqing,&Xu, Long.(2015).Machine Learning-Based Coding Unit Depth Decisions for Flexible Complexity Allocation in High Efficiency Video Coding.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(7),2225-2238. |
| MLA | Zhang, Yun,et al."Machine Learning-Based Coding Unit Depth Decisions for Flexible Complexity Allocation in High Efficiency Video Coding".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.7(2015):2225-2238. |
入库方式： OAI收割
来源：国家天文台
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