Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/642965
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dc.contributor.authorKe Guen_US
dc.contributor.authorShiqi Wangen_US
dc.contributor.authorGuangtao Zhaien_US
dc.contributor.authorWeisi Linen_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorWenjun Zhangen_US
dc.date.accessioned2020-05-20T10:06:17Z-
dc.date.available2020-05-20T10:06:17Z-
dc.date.issued2016en_US
dc.identifier.issn0018-9316en_US
dc.identifier.issn1557-9611en_US
dc.identifier.other10.1109/TBC.2015.2511624en_US
dc.identifier.urihttp://localhost/handle/Hannan/176171en_US
dc.identifier.urihttp://localhost/handle/Hannan/642965-
dc.description.abstractImage quality assessment (IQA) has been an active research area during last decades. Many existing objective IQA models share a similar two-step structure with measuring local distortion before pooling. Compared with the rapid development for local distortion measurement, seldom effort has been made dedicated to effective pooling schemes. In this paper, we design a new pooling model via the analysis of distortion distribution affected by image content and distortion. That is, distributions of distortion position, distortion intensity, frequency changes, and histogram changes are comprehensively considered to infer an overall quality score. Experimental results conducted on four large-scale image quality databases (LIVE, TID2008, CSIQ, and CCID2014) concluded with three valuable findings. First, the proposed technique leads to consistent improvement in the IQA performance for studied local distortion measures. Second, relative to the traditional pooling, the performance gain of our algorithm is beyond 15% on average. Third, the best overall performance made by the proposed strategy outperforms state-of-the-art competitors.en_US
dc.publisherIEEEen_US
dc.relation.haspart7390011.pdfen_US
dc.subjectdistortion distribution|ranking-based weighting (RW)|entropy gain multiplier (EGM)|frequency variation-induced adjuster (FVA)|multi-scale (MS)|Image quality assessment (IQA)|poolingen_US
dc.titleAnalysis of Distortion Distribution for Pooling in Image Quality Predictionen_US
dc.typeArticleen_US
dc.journal.volume62en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Broadcastingen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorKe Guen_US
dc.contributor.authorShiqi Wangen_US
dc.contributor.authorGuangtao Zhaien_US
dc.contributor.authorWeisi Linen_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorWenjun Zhangen_US
dc.date.accessioned2020-05-20T10:06:17Z-
dc.date.available2020-05-20T10:06:17Z-
dc.date.issued2016en_US
dc.identifier.issn0018-9316en_US
dc.identifier.issn1557-9611en_US
dc.identifier.other10.1109/TBC.2015.2511624en_US
dc.identifier.urihttp://localhost/handle/Hannan/176171en_US
dc.identifier.urihttp://localhost/handle/Hannan/642965-
dc.description.abstractImage quality assessment (IQA) has been an active research area during last decades. Many existing objective IQA models share a similar two-step structure with measuring local distortion before pooling. Compared with the rapid development for local distortion measurement, seldom effort has been made dedicated to effective pooling schemes. In this paper, we design a new pooling model via the analysis of distortion distribution affected by image content and distortion. That is, distributions of distortion position, distortion intensity, frequency changes, and histogram changes are comprehensively considered to infer an overall quality score. Experimental results conducted on four large-scale image quality databases (LIVE, TID2008, CSIQ, and CCID2014) concluded with three valuable findings. First, the proposed technique leads to consistent improvement in the IQA performance for studied local distortion measures. Second, relative to the traditional pooling, the performance gain of our algorithm is beyond 15% on average. Third, the best overall performance made by the proposed strategy outperforms state-of-the-art competitors.en_US
dc.publisherIEEEen_US
dc.relation.haspart7390011.pdfen_US
dc.subjectdistortion distribution|ranking-based weighting (RW)|entropy gain multiplier (EGM)|frequency variation-induced adjuster (FVA)|multi-scale (MS)|Image quality assessment (IQA)|poolingen_US
dc.titleAnalysis of Distortion Distribution for Pooling in Image Quality Predictionen_US
dc.typeArticleen_US
dc.journal.volume62en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Broadcastingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7390011.pdf1.88 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKe Guen_US
dc.contributor.authorShiqi Wangen_US
dc.contributor.authorGuangtao Zhaien_US
dc.contributor.authorWeisi Linen_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorWenjun Zhangen_US
dc.date.accessioned2020-05-20T10:06:17Z-
dc.date.available2020-05-20T10:06:17Z-
dc.date.issued2016en_US
dc.identifier.issn0018-9316en_US
dc.identifier.issn1557-9611en_US
dc.identifier.other10.1109/TBC.2015.2511624en_US
dc.identifier.urihttp://localhost/handle/Hannan/176171en_US
dc.identifier.urihttp://localhost/handle/Hannan/642965-
dc.description.abstractImage quality assessment (IQA) has been an active research area during last decades. Many existing objective IQA models share a similar two-step structure with measuring local distortion before pooling. Compared with the rapid development for local distortion measurement, seldom effort has been made dedicated to effective pooling schemes. In this paper, we design a new pooling model via the analysis of distortion distribution affected by image content and distortion. That is, distributions of distortion position, distortion intensity, frequency changes, and histogram changes are comprehensively considered to infer an overall quality score. Experimental results conducted on four large-scale image quality databases (LIVE, TID2008, CSIQ, and CCID2014) concluded with three valuable findings. First, the proposed technique leads to consistent improvement in the IQA performance for studied local distortion measures. Second, relative to the traditional pooling, the performance gain of our algorithm is beyond 15% on average. Third, the best overall performance made by the proposed strategy outperforms state-of-the-art competitors.en_US
dc.publisherIEEEen_US
dc.relation.haspart7390011.pdfen_US
dc.subjectdistortion distribution|ranking-based weighting (RW)|entropy gain multiplier (EGM)|frequency variation-induced adjuster (FVA)|multi-scale (MS)|Image quality assessment (IQA)|poolingen_US
dc.titleAnalysis of Distortion Distribution for Pooling in Image Quality Predictionen_US
dc.typeArticleen_US
dc.journal.volume62en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Broadcastingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7390011.pdf1.88 MBAdobe PDFThumbnail
Preview File