Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/219988
Title: No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization
Authors: Ke Gu;Weisi Lin;Guangtao Zhai;Xiaokang Yang;Wenjun Zhang;Chang Wen Chen
Year: 2017
Publisher: IEEE
Abstract: The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we lirst roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum information via visual saliency. From global perspective, we compare the image histogram with the uniformly distributed histogram of maximum information via the symmetric Kullback-Leibler divergence. The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations. Thorough experiments on live databases/subsets demonstrate the superiority of our training-free blind technique over state-of-the-art fulland no-reference IQA methods. Furthermore, the proposed model is also applied to amend the performance of general-purpose blind quality metrics to a sizable margin.
URI: http://localhost/handle/Hannan/219988
volume: 47
issue: 12
More Information: 4559,
4565
Appears in Collections:2017

Files in This Item:
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7492198.pdf990.67 kBAdobe PDF
Title: No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization
Authors: Ke Gu;Weisi Lin;Guangtao Zhai;Xiaokang Yang;Wenjun Zhang;Chang Wen Chen
Year: 2017
Publisher: IEEE
Abstract: The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we lirst roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum information via visual saliency. From global perspective, we compare the image histogram with the uniformly distributed histogram of maximum information via the symmetric Kullback-Leibler divergence. The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations. Thorough experiments on live databases/subsets demonstrate the superiority of our training-free blind technique over state-of-the-art fulland no-reference IQA methods. Furthermore, the proposed model is also applied to amend the performance of general-purpose blind quality metrics to a sizable margin.
URI: http://localhost/handle/Hannan/219988
volume: 47
issue: 12
More Information: 4559,
4565
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7492198.pdf990.67 kBAdobe PDF
Title: No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization
Authors: Ke Gu;Weisi Lin;Guangtao Zhai;Xiaokang Yang;Wenjun Zhang;Chang Wen Chen
Year: 2017
Publisher: IEEE
Abstract: The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we lirst roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum information via visual saliency. From global perspective, we compare the image histogram with the uniformly distributed histogram of maximum information via the symmetric Kullback-Leibler divergence. The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations. Thorough experiments on live databases/subsets demonstrate the superiority of our training-free blind technique over state-of-the-art fulland no-reference IQA methods. Furthermore, the proposed model is also applied to amend the performance of general-purpose blind quality metrics to a sizable margin.
URI: http://localhost/handle/Hannan/219988
volume: 47
issue: 12
More Information: 4559,
4565
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7492198.pdf990.67 kBAdobe PDF