A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

doi:10.32604/cmes.2023.025832

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	A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing
	Zhang, Yanjun 3; He, Yongqiang 4; Zhang, Jingbo 3; Zhao, Yaru 1; Cui, Zhihua 3; Zhang, Wensheng 2
刊名	CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
	2023
卷号	137 期号:1 页码:363-383
关键词	Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm
ISSN号	1526-1492
DOI	10.32604/cmes.2023.025832
通讯作者	Cui, Zhihua(cuizhihua@tyust.edu.cn)
英文摘要	The video compression sensing method based on multi hypothesis has attracted extensive attention in the research of video codec with limited resources. However, the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging task. To resolve this problem, this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction optimization method. It mainly includes the optimization of prediction blocks (OPBS), the selection of search windows and the use of neighborhood information. Specifically, the OPBS consists of two parts: the selection of blocks and the optimization of prediction blocks. We combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video sequence. In addition, most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on performance. Therefore, Block-level search window (BSW) is constructed to cover the position of the optimal hypothesis block as much as possible. To maximize the availability of reference frames, Nearby reference frame information (NRFI) is designed to reconstruct the current block. The proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction performance. Experimental results show that the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.
WOS关键词	RESIDUAL RECONSTRUCTION
WOS研究方向	Engineering ; Mathematics
语种	英语
出版者	TECH SCIENCE PRESS
WOS记录号	WOS:001033059300015
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53813]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Cui, Zhihua
作者单位	1.Beijing Univ Technol, Sch Comp Sci, Beijing 100124, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100049, Peoples R China 3.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan 030024, Peoples R China 4.Shanxi Inst Technol, Dept Big Data & Intelligent Engn, Yangquan 045000, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yanjun,He, Yongqiang,Zhang, Jingbo,et al. A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing[J]. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES,2023,137(1):363-383.
APA	Zhang, Yanjun,He, Yongqiang,Zhang, Jingbo,Zhao, Yaru,Cui, Zhihua,&Zhang, Wensheng.(2023).A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing.CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES,137(1),363-383.
MLA	Zhang, Yanjun,et al."A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing".CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 137.1(2023):363-383.