No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference

No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference Mohammad-Ali Mahmoudpour, Saeed Mahmoudpour, Kian Jafari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7008217/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Contrast change is an important factor that affects the quality of images. During image capturing, unfavorable lighting conditions can cause contrast change and visual quality loss. While various methods have been proposed to assess the quality of images under different distortions such as blur and noise, contrast distortion has been largely overlooked as its visual impact and properties are different from other conventional types of distortions. In this paper, we propose a no-reference image quality assessment (NR-IQA) metric for contrast-distorted images. Using a set of contrast enhancement algorithms, we aim to generate pseudo-reference images that are visually close to the actual reference image, such that the NR problem is transformed to a Full-reference (FR) assessment with higher accuracy. To this end, a large dataset of contrast-enhanced images is produced to train a classification network that can select the most suitable contrast enhancement algorithm – based on image content and distortion – for pseudo-reference image generation. Finally, the evaluation is performed in the FR manner to assess the quality difference between the contrast-enhanced (pseudo-reference) and degraded images. Performance evaluation of the proposed method on three databases containing contrast distortions (CCID2014, TID2013, and CSIQ), indicates the promising performance of the proposed method. Image Quality Assessment Contrast Enhancement Contrast Distortion Neural Networks Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7008217","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482498800,"identity":"f69fb9f7-ef1c-4b73-9971-55cfb521db0d","order_by":0,"name":"Mohammad-Ali Mahmoudpour","email":"data:image/png;base64,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","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad-Ali","middleName":"","lastName":"Mahmoudpour","suffix":""},{"id":482498801,"identity":"c99dd1e4-2b07-4c9a-bb45-b59dc2e3477a","order_by":1,"name":"Saeed Mahmoudpour","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Mahmoudpour","suffix":""},{"id":482498802,"identity":"e7ceaed8-b231-429e-a9cc-82f020539bab","order_by":2,"name":"Kian Jafari","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Kian","middleName":"","lastName":"Jafari","suffix":""}],"badges":[],"createdAt":"2025-06-30 08:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7008217/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7008217/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92458186,"identity":"e315362b-9454-4b6c-8d4a-47963fdc7bf0","added_by":"auto","created_at":"2025-09-30 02:53:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":711286,"visible":true,"origin":"","legend":"","description":"","filename":"IQA7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7008217/v1_covered_0c4c750b-dac2-43d4-b5fc-95e370d4573e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Image Quality Assessment, Contrast Enhancement, Contrast Distortion, Neural Networks, Classification","lastPublishedDoi":"10.21203/rs.3.rs-7008217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7008217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eContrast change is an important factor that affects the quality of images. During image capturing, unfavorable lighting conditions can cause contrast change and visual quality loss. While various methods have been proposed to assess the quality of images under different distortions such as blur and noise, contrast distortion has been largely overlooked as its visual impact and properties are different from other conventional types of distortions. In this paper, we propose a no-reference image quality assessment (NR-IQA) metric for contrast-distorted images. Using a set of contrast enhancement algorithms, we aim to generate pseudo-reference images that are visually close to the actual reference image, such that the NR problem is transformed to a Full-reference (FR) assessment with higher accuracy. To this end, a large dataset of contrast-enhanced images is produced to train a classification network that can select the most suitable contrast enhancement algorithm – based on image content and distortion – for pseudo-reference image generation. 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