Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/219988
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dc.contributor.authorKe Guen_US
dc.contributor.authorWeisi Linen_US
dc.contributor.authorGuangtao Zhaien_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorWenjun Zhangen_US
dc.contributor.authorChang Wen Chenen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:15:03Z-
dc.date.available2020-04-06T08:15:03Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TCYB.2016.2575544en_US
dc.identifier.urihttp://localhost/handle/Hannan/219988-
dc.description.abstractThe 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.en_US
dc.format.extent4559,en_US
dc.format.extent4565en_US
dc.publisherIEEEen_US
dc.relation.haspart7492198.pdfen_US
dc.titleNo-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximizationen_US
dc.typeArticleen_US
dc.journal.volume47en_US
dc.journal.issue12en_US
Appears in Collections:2017

Files in This Item:
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7492198.pdf990.67 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKe Guen_US
dc.contributor.authorWeisi Linen_US
dc.contributor.authorGuangtao Zhaien_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorWenjun Zhangen_US
dc.contributor.authorChang Wen Chenen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:15:03Z-
dc.date.available2020-04-06T08:15:03Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TCYB.2016.2575544en_US
dc.identifier.urihttp://localhost/handle/Hannan/219988-
dc.description.abstractThe 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.en_US
dc.format.extent4559,en_US
dc.format.extent4565en_US
dc.publisherIEEEen_US
dc.relation.haspart7492198.pdfen_US
dc.titleNo-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximizationen_US
dc.typeArticleen_US
dc.journal.volume47en_US
dc.journal.issue12en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7492198.pdf990.67 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKe Guen_US
dc.contributor.authorWeisi Linen_US
dc.contributor.authorGuangtao Zhaien_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorWenjun Zhangen_US
dc.contributor.authorChang Wen Chenen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:15:03Z-
dc.date.available2020-04-06T08:15:03Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TCYB.2016.2575544en_US
dc.identifier.urihttp://localhost/handle/Hannan/219988-
dc.description.abstractThe 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.en_US
dc.format.extent4559,en_US
dc.format.extent4565en_US
dc.publisherIEEEen_US
dc.relation.haspart7492198.pdfen_US
dc.titleNo-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximizationen_US
dc.typeArticleen_US
dc.journal.volume47en_US
dc.journal.issue12en_US
Appears in Collections:2017

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