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Analysis of Probability Density Functions in Existing No-Reference Image Quality Assessment Algorithm for Contrast-Distorted Images

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dc.citedby4
dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorDer C.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorHammad B.T.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid7410253413en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57193327622en_US
dc.date.accessioned2023-05-29T07:24:18Z
dc.date.available2023-05-29T07:24:18Z
dc.date.issued2019
dc.descriptionBells; Correlation methods; Database systems; Entropy; Higher order statistics; Probability; Probability density function; Quality control; Statistics; Distorted images; Image quality assessment; Kurtosis and skewness; Natural scene images; No-reference image quality assessments; Pearson correlation coefficients; Probability density function (pdf); Public image database; Image qualityen_US
dc.description.abstractAmongst all distortion types, contrast change is very crucial for visual perception of image quality. Contrast distortion may be caused by poor lighting condition and poor quality image acquisition device. Contrast-distorted image (CDI) is defined as image with low dynamic range of brightness. Most of existing 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 CDI, e.g. 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). The five features used in NR-IQA-CDI are the global spatial statistics of an image including the mean, standard deviation, entropy, kurtosis and skewness. The statistical model or the Probability Density Function (PDF) for each of the given moment features were estimated using a public image database with large number of natural scene images. Because of 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, thus motivate us to further investigated to detect the gabs in existing NR-IQA-CDI. The paper can address the problem of existing NR-IQA-CDI which the bell-curve like probability density function (pdf) of the contrast related features like standard deviation and entropy does not correlate well with the monotonic relation between the contrast features and the perceived contrast level. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8837095
dc.identifier.doi10.1109/ICSGRC.2019.8837095
dc.identifier.epage137
dc.identifier.scopus2-s2.0-85073247540
dc.identifier.spage133
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073247540&doi=10.1109%2fICSGRC.2019.8837095&partnerID=40&md5=9b2638e6f81dacfd8df1ec9e091e028f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24534
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleICSGRC 2019 - 2019 IEEE 10th Control and System Graduate Research Colloquium, Proceeding
dc.titleAnalysis of Probability Density Functions in Existing No-Reference Image Quality Assessment Algorithm for Contrast-Distorted Imagesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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