Binary and Multi-Class Learning Based Low Complexity Optimization for HEVC Encoding | |
Kwong, Sam; Zhu, Linwei; Zhang, Yun; Pan, Zhaoqing; Wang, Ran; Peng, Zongju. | |
刊名 | IEEE Transactions on Broadcasting
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2017 | |
文献子类 | 期刊论文 |
英文摘要 | High Efficiency Video Coding (HEVC) improves the compression efficiency at the cost of high computational complexity by using the quad-tree coding unit (CU) structure and variable prediction unit (PU) modes. To minimize the HEVC encoding complexity while maintaining its compression efficiency, a binary and multi-class support vector machine (SVM)-based fast HEVC encoding algorithm is presented in this paper. First, the processes of recursive CU decision and PU selection in HEVC are modeled as hierarchical binary classification and multi-class classification structures. Second, according to the two classification structures, the CU decision and PU selection are optimized by binary and multi-class SVM, i.e., the CU and PU can be predicted directly via classifiers without intensive rate distortion (RD) cost calculation. In particular, to achieve better prediction performance, a learning method is proposed to combine the off-line machine learning (ML) mode and on-line ML mode for classifiers based on a multiple reviewers system. Additionally, the optimal parameters determination scheme is adopted for flexible complexity allocation under a given RD constraint. Experimental results show that the proposed method can achieve 68.3%, 67.3%, and 65.6% time saving on average while the values of Bjøntegaard delta peak signal-to-noise ratio are −0.093dB, −0.091dB, and −0.094dB and the values of Bjøntegaard delta bit rate are 4.191%, 3.842%, and 3.665% under low delay P main, low delay main, and random access configurations, respectively, when compared with the HEVC test model version HM 16.5. Meanwhile, the proposed method outperforms the state-of-the-art fast coding algorithms in terms of complexity reduction and RD performance. |
URL标识 | 查看原文 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.siat.ac.cn:8080/handle/172644/12473] ![]() |
专题 | 深圳先进技术研究院_数字所 |
作者单位 | IEEE Transactions on Broadcasting |
推荐引用方式 GB/T 7714 | Kwong, Sam,Zhu, Linwei,Zhang, Yun,et al. Binary and Multi-Class Learning Based Low Complexity Optimization for HEVC Encoding[J]. IEEE Transactions on Broadcasting ,2017. |
APA | Kwong, Sam,Zhu, Linwei,Zhang, Yun,Pan, Zhaoqing,Wang, Ran,&Peng, Zongju..(2017).Binary and Multi-Class Learning Based Low Complexity Optimization for HEVC Encoding.IEEE Transactions on Broadcasting . |
MLA | Kwong, Sam,et al."Binary and Multi-Class Learning Based Low Complexity Optimization for HEVC Encoding".IEEE Transactions on Broadcasting (2017). |
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