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Improve of contrast-distorted image quality assessment based on convolutional neural networks

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dc.citedby6
dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorDer C.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorMohamed M.A.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid7410253413en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57194596063en_US
dc.date.accessioned2023-05-29T07:29:26Z
dc.date.available2023-05-29T07:29:26Z
dc.date.issued2019
dc.description.abstractMany image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN). The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI. Copyright � 2019 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijece.v9i6.pp5604-5614
dc.identifier.epage5614
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85071679155
dc.identifier.spage5604
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071679155&doi=10.11591%2fijece.v9i6.pp5604-5614&partnerID=40&md5=bef693be6f57b633ce7290dd72f5a642
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24957
dc.identifier.volume9
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.titleImprove of contrast-distorted image quality assessment based on convolutional neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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