Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151370
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dc.contributor.authorWu, Jinjianen_US
dc.contributor.authorZeng, Jichenen_US
dc.contributor.authorDong, Weishengen_US
dc.contributor.authorShi, Guangmingen_US
dc.contributor.authorLin, Weisien_US
dc.date.accessioned2021-06-23T08:12:11Z-
dc.date.available2021-06-23T08:12:11Z-
dc.date.issued2019-
dc.identifier.citationWu, J., Zeng, J., Dong, W., Shi, G. & Lin, W. (2019). Blind image quality assessment with hierarchy : degradation from local structure to deep semantics. Journal of Visual Communication and Image Representation, 58, 353-362. https://dx.doi.org/10.1016/j.jvcir.2018.12.005en_US
dc.identifier.issn1047-3203en_US
dc.identifier.urihttps://hdl.handle.net/10356/151370-
dc.description.abstractThough blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Visual Communication and Image Representationen_US
dc.rights© 2018 Elsevier Inc. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleBlind image quality assessment with hierarchy : degradation from local structure to deep semanticsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.jvcir.2018.12.005-
dc.identifier.scopus2-s2.0-85058441665-
dc.identifier.volume58en_US
dc.identifier.spage353en_US
dc.identifier.epage362en_US
dc.subject.keywordsBlind Image Quality Assessmenten_US
dc.subject.keywordsHierarchical Featureen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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