Westland, S. orcid.org/0000-0003-3480-4755, Finlayson, G., Lai, P. orcid.org/0000-0002-8095-5928 et al. (3 more authors) (Cover date: September/October 2024) A computational method for predicting color palette discriminability. Color Research & Application, 49 (5). pp. 465-473. ISSN 0361-2317
Abstract
Automatic analysis of images is increasingly being used to generate color insights and this has led to various methods for generating palettes. Several studies have recently been published that explore methods to predict the visual similarity between pairs of palettes and these methods are often used to evaluate different generative methods. This work is concerned with being able to predict visual similarity between color palettes. Three data sets (two of which were previously published) are used to evaluate two methods for predicting visual similarity between palettes. A novel palette-difference metric (based on the Hungarian algorithm) is compared to the previously published minimum color difference model (MICD) and was found to agree better with the visual data for two of the three data sets. Agreement between models and visual data was also better for CIEDE2000 (1, 2) than for CIELAB metrics.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Color Research and Application published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | color difference, color palette, modeling, psychophysics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Funding Information: | Funder Grant number AHRC (Arts & Humanities Research Council) AH/S002812/1 The Clothworkers' Company Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Apr 2024 12:23 |
Last Modified: | 23 Sep 2024 14:15 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/col.22927 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:211043 |