Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142323
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dc.contributor.authorZhang, Yabinen_US
dc.contributor.authorLin, Weisien_US
dc.contributor.authorLi, Qiaohongen_US
dc.contributor.authorCheng, Wentaoen_US
dc.contributor.authorZhang, Xinfengen_US
dc.date.accessioned2020-06-19T03:50:52Z-
dc.date.available2020-06-19T03:50:52Z-
dc.date.issued2017-
dc.identifier.citationZhang, Y., Lin, W., Li, Q., Cheng, W., & Zhang, X. (2018). Multiple-level feature-based measure for retargeted image quality. IEEE Transactions on Image Processing, 27(1), 451-463. doi:10.1109/TIP.2017.2761556en_US
dc.identifier.issn1057-7149en_US
dc.identifier.urihttps://hdl.handle.net/10356/142323-
dc.description.abstractObjective image retargeting quality assessment aims to use computational models to predict the retargeted image quality consistent with subjective perception. In this paper, we propose a multiple-level feature (MLF)-based quality measure to predict the perceptual quality of retargeted images. We first provide an in-depth analysis on the low-level aspect ratio similarity feature, and then propose a mid-level edge group similarity feature, to better address the shape/structure related distortion. Furthermore, a high-level face block similarity feature is designed to deal with sensitive region deformation. The multiple-level features are complementary as they quantify different aspects of quality degradation in the retargeted image, and the MLF measure learned by regression is used to predict the perceptual quality of retargeted images. Extensive experimental results performed on two public benchmark databases demonstrate that the proposed MLF measure achieves higher quality prediction accuracy than the existing relevant state-of-the-art quality measures.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)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.titleMultiple-level feature-based measure for retargeted image qualityen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TIP.2017.2761556-
dc.identifier.pmid28991745-
dc.identifier.scopus2-s2.0-85038256649-
dc.identifier.issue1en_US
dc.identifier.volume27en_US
dc.identifier.spage451en_US
dc.identifier.epage463en_US
dc.subject.keywordsRetargeted Image Qualityen_US
dc.subject.keywordsEdge Group Similarityen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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