Subtractive Color Mixture Computation

TL;DR

This paper introduces a computational method to model subtractive color mixing using only RGB values by transforming them into spectral distributions and applying a weighted mean, enabling practical applications in computer graphics.

Contribution

It proposes a novel spectral transformation and mixture approach that accurately predicts subtractive color outcomes from RGB data without extensive measurements.

Findings

Provides realistic subtractive mixture colors from RGB inputs

Requires modest computational effort

No need for spectrophotometric measurements

Abstract

Modeling subtractive color mixture (e.g., the way that paints mix) is difficult when working with colors described only by three-dimensional color space values, such as RGB. Although RGB values are sufficient to describe a specific color sensation, they do not contain enough information to predict the RGB color that would result from a subtractive mixture of two specified RGB colors. Methods do exist for accurately modeling subtractive mixture, such as the Kubelka-Munk equations, but require extensive spectrophotometric measurements of the mixed components, making them unsuitable for many computer graphics applications. This paper presents a strategy for modeling subtractive color mixture given only the RGB information of the colors being mixed, written for a general audience. The RGB colors are first transformed to generic, representative spectral distributions, and then this spectral information is used to perform the subtractive mixture, using the weighted arithmetic-geometric mean. This strategy provides reasonable, representative subtractive mixture colors with only modest computational effort and no experimental measurements. As such, it provides a useful way to model subtractive color mixture in computer graphics applications.