Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140107
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dc.contributor.authorJiang, Qiupingen_US
dc.contributor.authorShao, Fengen_US
dc.contributor.authorLin, Weisien_US
dc.contributor.authorJiang, Gangyien_US
dc.date.accessioned2020-05-26T07:56:20Z-
dc.date.available2020-05-26T07:56:20Z-
dc.date.issued2017-
dc.identifier.citationJiang, Q., Shao, F., Lin, W., & Jiang, G. (2018). Learning sparse representation for objective image retargeting quality assessment. IEEE Transactions on Cybernetics, 48(4), 1276-1289. doi:10.1109/TCYB.2017.2690452en_US
dc.identifier.issn2168-2275en_US
dc.identifier.urihttps://hdl.handle.net/10356/140107-
dc.description.abstractThe goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment. The principle idea is to extract distortion sensitive features from one image (e.g., retargeted image) and further investigate how many of these features are preserved or changed in another one (e.g., source image) to measure the perceptual similarity between them. To create a compact and robust feature representation, we learn two overcomplete dictionaries to represent the distortion sensitive features of an image. Features including local geometric structure and global context information are both addressed in the proposed framework. The intrinsic discriminative power of sparse representation is then exploited to measure the similarity between the source and retargeted images. Finally, individual quality scores are fused into an overall quality by a typical regression method. Experimental results on several databases have demonstrated the superiority of the proposed method.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Cyberneticsen_US
dc.rights© 2017 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleLearning sparse representation for objective image retargeting quality assessmenten_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchCentre for Multimedia and Network Technologyen_US
dc.identifier.doi10.1109/TCYB.2017.2690452-
dc.identifier.pmid28422677-
dc.identifier.scopus2-s2.0-85018494385-
dc.identifier.issue4en_US
dc.identifier.volume48en_US
dc.identifier.spage1276en_US
dc.identifier.epage1289en_US
dc.subject.keywordsGlobal Context Information (GCI)en_US
dc.subject.keywordsImage Retargeting Quality Assessment (IRQA)en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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