中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Online Learning-Based Multi-Stage Complexity Control for Live Video Coding
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Online Learning-Based Multi-Stage Complexity Control for Live Video Coding
Huang, Chao; Peng, Zongju; Xu, Yong; Chen, Fen; Jiang, Qiuping; Zhang, Yun; Jiang, Gangyi; Ho, Yo-Sung
2021
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
Subtype期刊论文
AbstractHigh Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.
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Indexed BySCI
Language英语
EI Accession Number高性能
Document Type期刊论文
Identifierhttp://ir.siat.ac.cn/handle/172644/17877
Collection数字所
Recommended Citation
GB/T 7714
Huang, Chao,Peng, Zongju,Xu, Yong,et al. Online Learning-Based Multi-Stage Complexity Control for Live Video Coding[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021.
APA Huang, Chao.,Peng, Zongju.,Xu, Yong.,Chen, Fen.,Jiang, Qiuping.,...&Ho, Yo-Sung.(2021).Online Learning-Based Multi-Stage Complexity Control for Live Video Coding.IEEE TRANSACTIONS ON IMAGE PROCESSING.
MLA Huang, Chao,et al."Online Learning-Based Multi-Stage Complexity Control for Live Video Coding".IEEE TRANSACTIONS ON IMAGE PROCESSING (2021).
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