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Contrast-distorted image quality assessment based on curvelet domain features

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dc.citedby2
dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorDer C.S.en_US
dc.contributor.authorHammad B.T.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid7410253413en_US
dc.contributor.authorid57193327622en_US
dc.contributor.authorid36682671900en_US
dc.date.accessioned2023-05-29T09:07:29Z
dc.date.available2023-05-29T09:07:29Z
dc.date.issued2021
dc.description.abstractContrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the Pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features. � 2021 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijece.v11i3.pp2595-2603
dc.identifier.epage2603
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85101166700
dc.identifier.spage2595
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101166700&doi=10.11591%2fijece.v11i3.pp2595-2603&partnerID=40&md5=ee799157eceb69740c12e3ca3874a721
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26181
dc.identifier.volume11
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Electrical and Computer Engineering
dc.titleContrast-distorted image quality assessment based on curvelet domain featuresen_US
dc.typeArticleen_US
dspace.entity.typePublication
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