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No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessmen...

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Autor principal: Varga, Domonkos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224559/
https://www.ncbi.nlm.nih.gov/pubmed/35735972
http://dx.doi.org/10.3390/jimaging8060173
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author Varga, Domonkos
author_facet Varga, Domonkos
author_sort Varga, Domonkos
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description With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings.
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spelling pubmed-92245592022-06-24 No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features Varga, Domonkos J Imaging Article With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings. MDPI 2022-06-19 /pmc/articles/PMC9224559/ /pubmed/35735972 http://dx.doi.org/10.3390/jimaging8060173 Text en © 2022 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Varga, Domonkos
No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
title No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
title_full No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
title_fullStr No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
title_full_unstemmed No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
title_short No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
title_sort no-reference quality assessment of authentically distorted images based on local and global features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224559/
https://www.ncbi.nlm.nih.gov/pubmed/35735972
http://dx.doi.org/10.3390/jimaging8060173
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