Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150418
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dc.contributor.authorTang, Zhisenen_US
dc.contributor.authorZheng, Yuanlinen_US
dc.contributor.authorGu, Keen_US
dc.contributor.authorLiao, Kaiyangen_US
dc.contributor.authorWang, Weien_US
dc.contributor.authorYu, Miaomiaoen_US
dc.date.accessioned2021-05-31T01:09:47Z-
dc.date.available2021-05-31T01:09:47Z-
dc.date.issued2019-
dc.identifier.citationTang, Z., Zheng, Y., Gu, K., Liao, K., Wang, W. & Yu, M. (2019). Full-reference image quality assessment by combining features in spatial and frequency domains. IEEE Transactions On Broadcasting, 65(1), 138-151. https://dx.doi.org/10.1109/TBC.2018.2871376en_US
dc.identifier.issn0018-9316en_US
dc.identifier.other0000-0001-9888-928X-
dc.identifier.other0000-0001-5540-3235-
dc.identifier.other0000-0002-6175-4602-
dc.identifier.urihttps://hdl.handle.net/10356/150418-
dc.description.abstractObjective image quality assessment employs mathematical and computational theory to objectively assess the quality of output images based on the human visual system (HVS). In this paper, a novel approach based on multifeature extraction in the spatial and frequency domains is proposed. We combine the gradient magnitude and phase congruency maps to generate a local structure (LS) map, which can perceive local structural distortions. The LS matches well with HVS and highlights differences with details. For complex visual information, such as texture and contrast sensitivity, we deploy the log-Gabor filter, and spatial frequency, respectively, to effectively capture their variations. Moreover, we employ the random forest (RF) to overcome the limitations of existing pooling methods. Compared with support vector regression, RF can obtain better prediction results. Extensive experimental results on the five benchmark databases indicate that the proposed method precedes all the state-of-the-art image quality assessment metrics in terms of prediction accuracy. In addition, the proposed method is in compliance with the subjective evaluations.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Broadcastingen_US
dc.rights© 2018 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleFull-reference image quality assessment by combining features in spatial and frequency domainsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TBC.2018.2871376-
dc.identifier.scopus2-s2.0-85054496419-
dc.identifier.issue1en_US
dc.identifier.volume65en_US
dc.identifier.spage138en_US
dc.identifier.epage151en_US
dc.subject.keywordsImage Quality Assessment (IQA)en_US
dc.subject.keywordsLog-Gabor Filteren_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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