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Using smooth metamers to estimate color appearance metrics for diverse color‐normal observers

Using smooth metamers to estimate color appearance metrics for diverse color‐normal observers Color‐normal subjects sometimes disagree about metameric matches involving highly structured spectral power distributions (SPDs), because their cone fundamentals differ slightly, but non‐negligibly. This has significant implications for the design of light sources and displays, so it should be estimated. We propose a broadly applicable estimation method based on a simple adaptive “front‐end” interface that can be used with any selected standard color appearance model. The interface accepts, as input, any set of color‐matching functions for the individual subject (eg, these could be that person's cone response functions) and also the associated tristimulus values for the test stimulus and also for the reference stimulus (ie, reference white). The interface converts these data into tristimulus values of the form used by the selected color appearance model (which could, eg, be X, Y, Z), while also carrying out the needed transform, which is based on an estimate of the subject's likely previous long‐term adaptations to their unique cone fundamentals. The selected standard color appearance model then provides color appearance data that are an estimate of the color appearance of the test stimulus, for that individual subject. This information has the advantage of being interpretable within that model's well‐known color space. The adaptive front end is based on the fact that, for any selected input SPD and the subject's unique color matching functions, there can be many different SPDs that are metameric for that individual. Since observer‐to‐observer color perception differences are minimized for spectrally smooth SPDs, smooth metamers predict color appearances reasonably accurately. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Color Research & Application Wiley

Using smooth metamers to estimate color appearance metrics for diverse color‐normal observers

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References (12)

Publisher
Wiley
Copyright
© 2022 Wiley Periodicals LLC.
ISSN
0361-2317
eISSN
1520-6378
DOI
10.1002/col.22749
Publisher site
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Abstract

Color‐normal subjects sometimes disagree about metameric matches involving highly structured spectral power distributions (SPDs), because their cone fundamentals differ slightly, but non‐negligibly. This has significant implications for the design of light sources and displays, so it should be estimated. We propose a broadly applicable estimation method based on a simple adaptive “front‐end” interface that can be used with any selected standard color appearance model. The interface accepts, as input, any set of color‐matching functions for the individual subject (eg, these could be that person's cone response functions) and also the associated tristimulus values for the test stimulus and also for the reference stimulus (ie, reference white). The interface converts these data into tristimulus values of the form used by the selected color appearance model (which could, eg, be X, Y, Z), while also carrying out the needed transform, which is based on an estimate of the subject's likely previous long‐term adaptations to their unique cone fundamentals. The selected standard color appearance model then provides color appearance data that are an estimate of the color appearance of the test stimulus, for that individual subject. This information has the advantage of being interpretable within that model's well‐known color space. The adaptive front end is based on the fact that, for any selected input SPD and the subject's unique color matching functions, there can be many different SPDs that are metameric for that individual. Since observer‐to‐observer color perception differences are minimized for spectrally smooth SPDs, smooth metamers predict color appearances reasonably accurately.

Journal

Color Research & Application – Wiley

Published: Jun 1, 2022

Keywords: adaptation; color appearance; estimation; individual cone fundamentals; individual variation

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