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No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features
The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions du...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321363/ http://dx.doi.org/10.3390/jimaging7070112 |
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author | Varga, Domonkos |
author_facet | Varga, Domonkos |
author_sort | Varga, Domonkos |
collection | PubMed |
description | The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ). |
format | Online Article Text |
id | pubmed-8321363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83213632021-08-26 No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features Varga, Domonkos J Imaging Article The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ). MDPI 2021-07-10 /pmc/articles/PMC8321363/ http://dx.doi.org/10.3390/jimaging7070112 Text en © 2021 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 Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
title | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
title_full | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
title_fullStr | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
title_full_unstemmed | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
title_short | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
title_sort | no-reference image quality assessment with multi-scale orderless pooling of deep features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321363/ http://dx.doi.org/10.3390/jimaging7070112 |
work_keys_str_mv | AT vargadomonkos noreferenceimagequalityassessmentwithmultiscaleorderlesspoolingofdeepfeatures |