Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142325
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dc.contributor.authorFang, Yumingen_US
dc.contributor.authorYan, Jiebinen_US
dc.contributor.authorLi, Leidaen_US
dc.contributor.authorWu, Jinjianen_US
dc.contributor.authorLin, Weisien_US
dc.date.accessioned2020-06-19T04:05:03Z-
dc.date.available2020-06-19T04:05:03Z-
dc.date.issued2017-
dc.identifier.citationFang, Y., Yan, J., Li, L., Wu, J., & Lin, W. (2018). No reference quality assessment for screen content images with both local and global feature representation. IEEE Transactions on Image Processing, 27(4), 1600-1610. doi:10.1109/TIP.2017.2781307en_US
dc.identifier.issn1057-7149en_US
dc.identifier.urihttps://hdl.handle.net/10356/142325-
dc.description.abstractIn this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Image Processingen_US
dc.rights© 2017 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleNo reference quality assessment for screen content images with both local and global feature representationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TIP.2017.2781307-
dc.identifier.pmid29324414-
dc.identifier.scopus2-s2.0-85038373470-
dc.identifier.issue4en_US
dc.identifier.volume27en_US
dc.identifier.spage1600en_US
dc.identifier.epage1610en_US
dc.subject.keywordsScreen Content Imageen_US
dc.subject.keywordsVisual Quality Assessmenten_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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