Enhancing Image Quality Prediction with Self-supervised Visual Masking
| dc.contributor.author | Çogalan, Ugur | en_US |
| dc.contributor.author | Bemana, Mojtaba | en_US |
| dc.contributor.author | Seidel, Hans-Peter | en_US |
| dc.contributor.author | Myszkowski, Karol | en_US |
| dc.contributor.editor | Bermano, Amit H. | en_US |
| dc.contributor.editor | Kalogerakis, Evangelos | en_US |
| dc.date.accessioned | 2024-04-30T09:09:00Z | |
| dc.date.available | 2024-04-30T09:09:00Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Full-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.number | 2 | |
| dc.description.sectionheaders | Sampling and Image Enhancement | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 43 | |
| dc.identifier.doi | 10.1111/cgf.15051 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 12 pages | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.15051 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf15051 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.rights | Attribution-NonCommercial 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.title | Enhancing Image Quality Prediction with Self-supervised Visual Masking | en_US |