Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/607449
Title: Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation
Authors: Xinfeng Zhang;Weisi Lin;Ruiqin Xiong;Xianming Liu;Siwei Ma;Wen Gao
subject: patch clustering|denoising|SVD|Block transform coding|low-rank|compression noise
Year: 2016
Publisher: IEEE
Abstract: Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft thresholding the singular values in singular value decomposition of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.
URI: http://localhost/handle/Hannan/139625
http://localhost/handle/Hannan/607449
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
Appears in Collections:2016

Files in This Item:
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Title: Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation
Authors: Xinfeng Zhang;Weisi Lin;Ruiqin Xiong;Xianming Liu;Siwei Ma;Wen Gao
subject: patch clustering|denoising|SVD|Block transform coding|low-rank|compression noise
Year: 2016
Publisher: IEEE
Abstract: Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft thresholding the singular values in singular value decomposition of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.
URI: http://localhost/handle/Hannan/139625
http://localhost/handle/Hannan/607449
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7506098.pdf5.13 MBAdobe PDFThumbnail
Preview File
Title: Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation
Authors: Xinfeng Zhang;Weisi Lin;Ruiqin Xiong;Xianming Liu;Siwei Ma;Wen Gao
subject: patch clustering|denoising|SVD|Block transform coding|low-rank|compression noise
Year: 2016
Publisher: IEEE
Abstract: Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft thresholding the singular values in singular value decomposition of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.
URI: http://localhost/handle/Hannan/139625
http://localhost/handle/Hannan/607449
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7506098.pdf5.13 MBAdobe PDFThumbnail
Preview File