Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing
Digital images can be distorted or contaminated by noise in various steps of image acquisition, transmission, and storage. Thus, the research of such algorithms, which can evaluate the perceptual quality of digital images consistent with human quality judgement, is a hot topic in the literature. In...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/8/8/224 |
| _version_ | 1797432034926264320 |
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| author | Domonkos Varga |
| author_facet | Domonkos Varga |
| author_sort | Domonkos Varga |
| collection | DOAJ |
| description | Digital images can be distorted or contaminated by noise in various steps of image acquisition, transmission, and storage. Thus, the research of such algorithms, which can evaluate the perceptual quality of digital images consistent with human quality judgement, is a hot topic in the literature. In this study, an image quality assessment (IQA) method is introduced that predicts the perceptual quality of a digital image by optimally combining several IQA metrics. To be more specific, an optimization problem is defined first using the weighted sum of a few IQA metrics. Subsequently, the optimal values of the weights are determined by minimizing the root mean square error between the predicted and ground-truth scores using the simulated annealing algorithm. The resulted optimization-based IQA metrics were assessed and compared to other state-of-the-art methods on four large, widely applied benchmark IQA databases. The numerical results empirically corroborate that the proposed approach is able to surpass other competing IQA methods. |
| first_indexed | 2024-03-09T09:55:27Z |
| format | Article |
| id | doaj.art-7d6c64d738004dbfa8af19e54d9e8325 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T09:55:27Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-7d6c64d738004dbfa8af19e54d9e83252023-12-01T23:51:14ZengMDPI AGJournal of Imaging2313-433X2022-08-018822410.3390/jimaging8080224Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated AnnealingDomonkos Varga0Ronin Institute, Montclair, NJ 07043, USADigital images can be distorted or contaminated by noise in various steps of image acquisition, transmission, and storage. Thus, the research of such algorithms, which can evaluate the perceptual quality of digital images consistent with human quality judgement, is a hot topic in the literature. In this study, an image quality assessment (IQA) method is introduced that predicts the perceptual quality of a digital image by optimally combining several IQA metrics. To be more specific, an optimization problem is defined first using the weighted sum of a few IQA metrics. Subsequently, the optimal values of the weights are determined by minimizing the root mean square error between the predicted and ground-truth scores using the simulated annealing algorithm. The resulted optimization-based IQA metrics were assessed and compared to other state-of-the-art methods on four large, widely applied benchmark IQA databases. The numerical results empirically corroborate that the proposed approach is able to surpass other competing IQA methods.https://www.mdpi.com/2313-433X/8/8/224full-reference image quality assessmentfeature selectionsimulated annealing |
| spellingShingle | Domonkos Varga Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing Journal of Imaging full-reference image quality assessment feature selection simulated annealing |
| title | Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing |
| title_full | Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing |
| title_fullStr | Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing |
| title_full_unstemmed | Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing |
| title_short | Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing |
| title_sort | full reference image quality assessment based on an optimal linear combination of quality measures selected by simulated annealing |
| topic | full-reference image quality assessment feature selection simulated annealing |
| url | https://www.mdpi.com/2313-433X/8/8/224 |
| work_keys_str_mv | AT domonkosvarga fullreferenceimagequalityassessmentbasedonanoptimallinearcombinationofqualitymeasuresselectedbysimulatedannealing |