Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/628842
Title: CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking
Authors: Jian Zhang;Ruiqin Xiong;Chen Zhao;Yongbing Zhang;Siwei Ma;Wen Gao
subject: optimization|low-rank|quantization constraint|Image deblocking|blocking artifact reduction
Year: 2016
Publisher: IEEE
Abstract: 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<sub>p</sub> (0 &lt;; p &lt;; 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.
URI: http://localhost/handle/Hannan/163652
http://localhost/handle/Hannan/628842
ISSN: 1057-7149
1941-0042
volume: 25
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7377084.pdf4.93 MBAdobe PDFThumbnail
Preview File
Title: CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking
Authors: Jian Zhang;Ruiqin Xiong;Chen Zhao;Yongbing Zhang;Siwei Ma;Wen Gao
subject: optimization|low-rank|quantization constraint|Image deblocking|blocking artifact reduction
Year: 2016
Publisher: IEEE
Abstract: 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<sub>p</sub> (0 &lt;; p &lt;; 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.
URI: http://localhost/handle/Hannan/163652
http://localhost/handle/Hannan/628842
ISSN: 1057-7149
1941-0042
volume: 25
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7377084.pdf4.93 MBAdobe PDFThumbnail
Preview File
Title: CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking
Authors: Jian Zhang;Ruiqin Xiong;Chen Zhao;Yongbing Zhang;Siwei Ma;Wen Gao
subject: optimization|low-rank|quantization constraint|Image deblocking|blocking artifact reduction
Year: 2016
Publisher: IEEE
Abstract: 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<sub>p</sub> (0 &lt;; p &lt;; 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.
URI: http://localhost/handle/Hannan/163652
http://localhost/handle/Hannan/628842
ISSN: 1057-7149
1941-0042
volume: 25
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7377084.pdf4.93 MBAdobe PDFThumbnail
Preview File