Enhancing Image Quality Prediction with Self-supervised Visual Masking

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dc.contributor.authorÇogalan, Uguren_US
dc.contributor.authorBemana, Mojtabaen_US
dc.contributor.authorSeidel, Hans-Peteren_US
dc.contributor.authorMyszkowski, Karolen_US
dc.contributor.editorBermano, Amit H.en_US
dc.contributor.editorKalogerakis, Evangelosen_US
dc.date.accessioned2024-04-30T09:09:00Z
dc.date.available2024-04-30T09:09:00Z
dc.date.issued2024
dc.description.abstractFull-reference image quality metrics (FR-IQMs) aim to measure the visual differences between a pair of reference and distorted images, with the goal of accurately predicting human judgments. However, existing FR-IQMs, including traditional ones like PSNR and SSIM and even perceptual ones such as HDR-VDP, LPIPS, and DISTS, still fall short in capturing the complexities and nuances of human perception. In this work, rather than devising a novel IQM model, we seek to improve upon the perceptual quality of existing FR-IQM methods. We achieve this by considering visual masking, an important characteristic of the human visual system that changes its sensitivity to distortions as a function of local image content. Specifically, for a given FR-IQM metric, we propose to predict a visual masking model that modulates reference and distorted images in a way that penalizes the visual errors based on their visibility. Since the ground truth visual masks are difficult to obtain, we demonstrate how they can be derived in a self-supervised manner solely based on mean opinion scores (MOS) collected from an FR-IQM dataset. Our approach results in enhanced FR-IQM metrics that are more in line with human prediction both visually and quantitatively.en_US
dc.description.number2
dc.description.sectionheadersSampling and Image Enhancement
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15051
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15051
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15051
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution-NonCommercial 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleEnhancing Image Quality Prediction with Self-supervised Visual Maskingen_US
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