CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking

doi:10.1109/TIP.2016.2515985

CORC > 北京大学 > 信息科学技术学院

	CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking
	Zhang, Jian ; Xiong, Ruiqin ; Zhao, Chen ; Zhang, Yongbing ; Ma, Siwei ; Gao, Wen
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2016
关键词	Image deblocking low-rank blocking artifact reduction optimization quantization constraint ARTIFACT REDUCTION TRANSFORM-DOMAIN QUANTIZATION CONSTRAINT SPARSE REPRESENTATION BLOCKING ARTIFACTS JPEG DECOMPRESSION QUALITY ASSESSMENT COMPRESSED IMAGES RESTORATION MINIMIZATION
DOI	10.1109/TIP.2016.2515985
英文摘要	Due to independent and coarse quantization of transform coefficients in each block, block-based transform coding usually introduces visually annoying blocking artifacts at low bitrates, which greatly prevents further bit reduction. To alleviate the conflict between bit reduction and quality preservation, deblocking as a post-processing strategy is an attractive and promising solution without changing existing codec. In this paper, in order to reduce blocking artifacts and obtain high-quality image, image deblocking is formulated as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking using constrained non-convex low-rank model is proposed. The l(p) (0 < p < 1) penalty function is extended on singular values of a matrix to characterize low-rank prior model rather than the nuclear norm, while the quantization constraint is explicitly transformed into the feasible solution space to constrain the non-convex low-rank optimization. Moreover, a new quantization noise model is developed, and an alternatively minimizing strategy with adaptive parameter adjustment is developed to solve the proposed optimization problem. This parameter-free advantage enables the whole algorithm more attractive and practical. Experiments demonstrate that the proposed image deblocking algorithm outperforms the current state-of-the-art methods in both the objective quality and the perceptual quality.; National Basic Research Program of China [2015CB351800]; Post-Doctoral Science Foundation of China [2015M580018]; National Natural Science Foundation of China [61322106, 61370114, 61421062, 61572047, U1201255, U1301257]; Shenzhen Peacock Plan; Cooperative Medianet Innovation Center; SCI(E); EI; ARTICLE; jian.zhang@pku.edu.cn; rqxiong@pku.edu.cn; zhaochen@pku.edu.cn; zhang.yongbing@sz.tsinghua.edu.cn; swma@pku.edu.cn; wgao@pku.edu.cn; 3; 1246-1259; 25
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/437646]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Jian,Xiong, Ruiqin,Zhao, Chen,et al. CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016.
APA	Zhang, Jian,Xiong, Ruiqin,Zhao, Chen,Zhang, Yongbing,Ma, Siwei,&Gao, Wen.(2016).CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking.IEEE TRANSACTIONS ON IMAGE PROCESSING.
MLA	Zhang, Jian,et al."CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking".IEEE TRANSACTIONS ON IMAGE PROCESSING (2016).