BIQABSF: Blind Image Quality Assessment Based on Statistical Features

BIQABSF: Blind Image Quality Assessment Based on Statistical Features | 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 Article BIQABSF: Blind Image Quality Assessment Based on Statistical Features Wentao Ji, Xing Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7860040/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract No-Reference Image Quality Assessment (NR-IQA) is challenging due to the absence of reference images. Natural Scene Statistics (NSS) provides a robust foundation for NR-IQA. However, BRISQUE relies on the statistical features of image intensity and imposes a strong zero-mean assumption, which may not hold in practice and could limit its performance. This paper proposes a novel NR-IQA method, the Blind Image Quality Assessment Based on Statistical Features (BIQABSF) algorithm, which introduces two key improvements. First, we utilize the statistical features of log-contrast (LC), which more closely align with human visual perception. Second, the distribution of the locally normalized LC was modeled using a generalized Gaussian distribution (GGD) without a zero-mean constraint, based on the empirical observation of its asymmetric nature. Specifically, we compute the Local Mean Subtracted Log-Contrast (LMSLC) and extract its distribution parameters as features for training a Support Vector Regression (SVR) model. Extensive experiments on the LIVE, TID2013, and CSIQ databases demonstrated that BIQABSF achieved highly competitive performance, even surpassing several deep learning-based methods in terms of efficiency and generalization, thereby demonstrating its potential as an efficient and generalizable solution for real-world applications. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Blind Image Quality Assessment Statistical analysis of images Log-contrast Support Vector Regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor invited by journal 21 Oct, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 15 Oct, 2025 First submitted to journal 14 Oct, 2025 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-7860040","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538574421,"identity":"6b838618-66c8-4ec3-a710-e430a1fc3f09","order_by":0,"name":"Wentao 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