An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment
Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framewo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/6/116 |
| _version_ | 1797594060529074176 |
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| author | Domonkos Varga |
| author_facet | Domonkos Varga |
| author_sort | Domonkos Varga |
| collection | DOAJ |
| description | Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework for FR-IQA that combines multiple metrics and tries to leverage the strength of each by formulating FR-IQA as an optimization problem. Following the idea of other fusion-based metrics, the perceptual quality of a test image is defined as the weighted product of several already existing, hand-crafted FR-IQA metrics. Unlike other methods, the weights are determined in an optimization-based framework and the objective function is defined to maximize the correlation and minimize the root mean square error between the predicted and ground-truth quality scores. The obtained metrics are evaluated on four popular benchmark IQA databases and compared to the state of the art. This comparison has revealed that the compiled fusion-based metrics are able to outperform other competing algorithms, including deep learning-based ones. |
| first_indexed | 2024-03-11T02:17:19Z |
| format | Article |
| id | doaj.art-59fc10f857e54c678742f817181ffe03 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T02:17:19Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-59fc10f857e54c678742f817181ffe032023-11-18T11:04:25ZengMDPI AGJournal of Imaging2313-433X2023-06-019611610.3390/jimaging9060116An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality AssessmentDomonkos Varga0Ronin Institute, Montclair, NJ 07043, USAGiven the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework for FR-IQA that combines multiple metrics and tries to leverage the strength of each by formulating FR-IQA as an optimization problem. Following the idea of other fusion-based metrics, the perceptual quality of a test image is defined as the weighted product of several already existing, hand-crafted FR-IQA metrics. Unlike other methods, the weights are determined in an optimization-based framework and the objective function is defined to maximize the correlation and minimize the root mean square error between the predicted and ground-truth quality scores. The obtained metrics are evaluated on four popular benchmark IQA databases and compared to the state of the art. This comparison has revealed that the compiled fusion-based metrics are able to outperform other competing algorithms, including deep learning-based ones.https://www.mdpi.com/2313-433X/9/6/116full-reference image quality assessmentoptimizationquality-aware features |
| spellingShingle | Domonkos Varga An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment Journal of Imaging full-reference image quality assessment optimization quality-aware features |
| title | An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment |
| title_full | An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment |
| title_fullStr | An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment |
| title_full_unstemmed | An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment |
| title_short | An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment |
| title_sort | optimization based family of predictive fusion based models for full reference image quality assessment |
| topic | full-reference image quality assessment optimization quality-aware features |
| url | https://www.mdpi.com/2313-433X/9/6/116 |
| work_keys_str_mv | AT domonkosvarga anoptimizationbasedfamilyofpredictivefusionbasedmodelsforfullreferenceimagequalityassessment AT domonkosvarga optimizationbasedfamilyofpredictivefusionbasedmodelsforfullreferenceimagequalityassessment |