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	Multi-Class Ranking Based Most Probable Prediction Unit Selection for HEVC Encoding
	Linwei Zhu; Sam Kwong;  Yun Zhang; Xu Wang; Siqi Wang
	2017
会议日期	2017
会议地点	Florida
英文摘要	In this paper, an incremental learning based multiclass Prediction Units (PUs) ranking approach is presented for High Efficiency Video Coding (HEVC) Rate-Distortion-Complexity (RDC) optimization. In particular, the process of PUs selection is formulated as a binary classification plus multi-class ranking task, and incremental learning is applied for classifier training to better exploit the information in the emerging training data. Furthermore, the proposed most probable PUs selection scheme is incorporated into a joint RDC optimization framework, where the complexity can be flexibly allocated targeting at minimizing computational cost under a constrained RD performance degradation. Experimental results demonstrate that the proposed approach can reduce 53.7% and 50.4% computational complexity on average under low delay P and random access configurations with ignorable RD performance degradation, which outperforms the state-of-the-art approaches in terms of RDC performance.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12640]
专题	深圳先进技术研究院_数字所
作者单位	2017
推荐引用方式 GB/T 7714	Linwei Zhu,Sam Kwong, Yun Zhang,et al. Multi-Class Ranking Based Most Probable Prediction Unit Selection for HEVC Encoding[C]. 见:. Florida. 2017.

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