AI-SSIM: Human-Centric Image Assessment through Pseudo-Reference Generation and Logical Consistency Analysis in AI-Generated Visuals

AI-SSIM: Human-Centric Image Assessment through Pseudo-Reference Generation and Logical Consistency Analysis in AI-Generated Visuals | 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 AI-SSIM: Human-Centric Image Assessment through Pseudo-Reference Generation and Logical Consistency Analysis in AI-Generated Visuals Muhammad Umair Danish, Memoona Aziz, Katarina Grolinger, Umair Rehman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5596193/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 We present AI-SSIM, a computational image metric for assessing the quality and logical consistency of AI-generated and real-world images. Traditional metrics like structural similarity index measure (SSIM) and multi-scale structural similarity index measure (MS-SSIM) require a ground-truth image, which is often unavailable in AI-generated imagery, and overlook key factors such as logical coherence and content usability. AI-SSIM addresses these gaps by employing advanced pre-trained models to generate a pseudo-reference image, convolution and attention layers to evaluate image quality, and adaptive pooling to minimize distortion during resizing pseudo-reference images. We also designed and statistically validated a multi-item questionnaire for assessing image quality. AI-SSIM was benchmarked against human scales and compared to both full-reference and no-reference metrics, where it demonstrated superior accuracy. The proposed metric has broad applicability, as it can compute scores in both scenarios where ground-truth images are either available or absent. AI-Generated Images Subjective Assessment Computational Image Metrics Image Quality Photorealism Human Perception Visual Assessment Generative Models 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5596193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":419915680,"identity":"90f2313f-562c-4441-8141-c561b6954551","order_by":0,"name":"Muhammad Umair Danish","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFCCBBA6YMdPspZkyQaStDAwHGDccIBYDbrtyc8ePPhzh9n4Ro7hA4YaO8JazM48MzdIbHvGZ3Yjx9iA4VgyEVpuJJhJJDYcZja7kbtNgrGBmRgt6d8kEv4cZtw8A6ylnhgtOWYSCWyHGTdIgLUcJsYvb8okEtsOJ0ucef/ZIOHYcSK0HE/fJvnjz2E7/va0xAcfaqoJa0EFCaRqGAWjYBSMglGAHQAASew+j653ZkAAAAAASUVORK5CYII=","orcid":"","institution":"Western University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Umair","lastName":"Danish","suffix":""},{"id":419915681,"identity":"22ca740b-2bae-41e8-b36b-a5d81cecd031","order_by":1,"name":"Memoona Aziz","email":"","orcid":"","institution":"Western University","correspondingAuthor":false,"prefix":"","firstName":"Memoona","middleName":"","lastName":"Aziz","suffix":""},{"id":419915682,"identity":"b171aaeb-7b7c-4751-969f-15ad60673b81","order_by":2,"name":"Katarina Grolinger","email":"","orcid":"","institution":"Western University","correspondingAuthor":false,"prefix":"","firstName":"Katarina","middleName":"","lastName":"Grolinger","suffix":""},{"id":419915683,"identity":"4f97f331-08ff-45bc-9113-037b6df546de","order_by":3,"name":"Umair Rehman","email":"","orcid":"","institution":"Western University","correspondingAuthor":false,"prefix":"","firstName":"Umair","middleName":"","lastName":"Rehman","suffix":""}],"badges":[],"createdAt":"2024-12-06 22:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5596193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5596193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77856770,"identity":"d0cf194e-851e-4629-b7c1-297c3f08818a","added_by":"auto","created_at":"2025-03-06 07:53:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304852,"visible":true,"origin":"","legend":"","description":"","filename":"AISSIMComplete.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5596193/v1_covered_cee1705f-4ffd-461a-bcf7-be6868302741.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-SSIM: Human-Centric Image Assessment through Pseudo-Reference Generation and Logical Consistency Analysis in AI-Generated Visuals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"AI-Generated Images, Subjective Assessment, Computational Image Metrics, Image Quality, Photorealism, Human Perception, Visual Assessment, Generative Models","lastPublishedDoi":"10.21203/rs.3.rs-5596193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5596193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe present AI-SSIM, a computational image metric for assessing the quality and logical consistency of AI-generated and real-world images. 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