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Evaluation of the IES Method for Evaluating Light Source Color Rendition in terms of Metamer Mismatching

Resource type
Date created
2016-11
Authors/Contributors
Author: Funt, Brian
Author: Hull, Ben
Abstract
The Illumination Engineering Society’s Rf color rendering index [IES TM-30-15, 201] is compared to the MMCRI [Metamer Mismatching as a Measure of the Color Rendering of Lights, Mirzaei & Funt, Proc. AIC 2015]. IES Rf is based on color differences using a special set of 99 surface reflectances; while, in contrast, MMCRI is based on all theoretically possible reflectances. The two indices evaluate many lights similarly, but the MMCRI ranks some lights—especially those having strong peaks and wavelength regions of minimal power—lower than does Rf. Is this difference in rating simply due to the fact that MMCRI uses all theoretically possible reflectances including step functions? A ‘practical’ version of MMCRI based on a set of 41 million real, measured spectral reflectances, rather than all theoretically possible reflectances, turns out to concur with the original MMCRI and shows that the disagreement between Rf and MMCRI is more fundamental. Overall, the present study suggests that Rf may overrate the color rendering properties of some lights; and, at the very least, indicate the type of lights upon which future psychophysical testing should concentrate.
Document
Description
Presented at the CIC'24 Color Imaging Conference, November 2016.
Published as
Funt, B., Hull, B., and Zhang, X. "Evaluation of the IES Method for Evaluating Light Source Color Rendition in terms of Metamer Mismatching." Proceedings of CIC'24 Color Imaging Conference, Society for Imaging Science and Technology, Nov. 2016
Publication title
Proceedings of the CIC'24 Color Imaging Conference, Society for Imaging Science and Technology,
Document title
Evaluation of the IES Method for Evaluating Light Source Color Rendition in terms of Metamer Mismatching
Date
2017
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
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Funt-Hull-Zhang_EvalofIES_Via_MMV_CIC2016.pdf 510.99 KB

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