On the Usefulness of Combined Metrics for 3D Image Quality Assessment

Okarma, Krzysztof

doi:10.1007/978-3-319-10662-5_17

Cited by 2 publications

(2 citation statements)

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“…Bakurov et al [ 53 ] revisited the classical SSIM [ 29 ] and MS-SSIM [ 34 ] metrics by applying evolutionary and swarm intelligence optimization methods to find optimal hyperparameters for SSIM and MS-SSIM instead of the original settings. Fusion-based metrics were also proposed for remote sensing images [ 54 ], stitched panoramic images [ 55 ], and 3D image quality assessment [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The development of objective FR-IQA algorithms can also involve fusion-based strategies that already take existing FR-IQA metrics and try to create a “super evaluator”. Recently, many complex fusion-based approaches have been published in the literature [ 16 , 17 , 18 , 19 ]. The main contribution to this paper is also a fusion-based approach.…”

Section: Introductionmentioning

confidence: 99%

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Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Varga

2022

J. Imaging

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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.

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