Improving calibration time and accuracy for situation-specific models of color differentiation

Abstract

Color vision deficiencies (CVDs) cause problems in situations where people need to differentiate the colors used in digital displays. Recoloring tools exist to reduce the problem, but these tools need a model of the user's color-differentiation ability in order to work. Situation-specific models are a recent approach that accounts for all of the factors affecting a person's CVD (including genetic, acquired, and environmental causes) by using calibration data to form the model. This approach works well, but requires repeated calibration - and the best available calibration procedure takes more than 30 minutes. To address this limitation, we have developed a new situation-specific model of human color differentiation (called ICD-2) that needs far fewer calibration trials. The new model uses a color space that better matches human color vision compared to the RGB space of the old model, and can therefore extract more meaning from each calibration test. In an empirical comparison, we found that ICD-2 is 24 times faster than the old approach, and had small but significant gains in accuracy. The efficiency of ICD-2 makes it feasible for situation-specific models of individual color differentiation to be used in the real world.