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Updated: Jun 18, 2025

Visualizing Visual Adaptation
Published on: April 24, 2017
Bridging instrumental and visual perception with improved color difference equations: A multi-center study
Sascha Hein1, Omnia Saleh2, Changjun Li3
1School of Design, University of Leeds, Leeds, UK.
Objectives:
This multicenter study aimed to evaluate visual-instrumental agreement of six color measurement devices and optimize three color difference equations using a dataset of visual color differences (∆V) from expert observers.
Methods:
A total of 154 expert observers from 16 sites across 5 countries participated, providing visual scaling on 26 sample pairs of artificial teeth using magnitude estimation. Three color difference equations (ΔE*ab, ∆E00, and CAM16-UCS) were tested. Optimization of all three equations was performed using device-specific weights, and the standardized residual sum of squares (STRESS) index was used to evaluate visual-instrumental agreement.
Results:
The ΔE*ab formula exhibited STRESS values from 18 to 40, with visual-instrumental agreement between 60 % and 82 %. The ∆E00 formula showed STRESS values from 26 to 32, representing visual-instrumental agreement of 68 % to 74 %. CAM16-UCS demonstrated STRESS values from 32 - 39, with visual-instrumental agreement between 61-68 %. Following optimization, STRESS values decreased for all three formulas, with ΔE' demonstrating average visual-instrumental agreement of 79 % and ∆E00 of 78 %. CAM16-UCS showed average visual-instrumental agreement of 76 % post optimization.
Significance:
Optimization of color difference equations notably improved visual-instrumental agreement, overshadowing device performance. The optimzed ΔE' formula demonstrated the best overall performance combining computational simplicty with outstanding visual-instrumental agreement.
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