Modelling, Measuring and Compensating Color Weak Vision

TL;DR

This paper develops a Riemann geometry-based framework to simulate and compensate for color perception differences in color weak vision, enabling more accurate color image perception for affected observers.

Contribution

It introduces a novel, global isometry-based method using normal coordinates for color compensation, improving upon previous local linearization approaches.

Findings

The method accurately simulates color perception of color weak observers.

The compensation method effectively aligns color perception between normal and weak vision.

Experimental results show improved perception consistency in SD tests.

Abstract

We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color weak observers. The two main applications are the simulation of the perception of a color weak observer for a color normal observer and the compensation of color images in a way that a color weak observer has approximately the same perception as a color normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticable-differences between color which are measured with the help of color-matching experiments. The constructed mappings are isometries of Riemann spaces that preserve the perceived color-differences for both observers. Among the two approaches to build such an isometry, we introduce normal coordinates in Riemann spaces as a tool to construct a global color-weak compensation map. Compared to previously used methods this method is free from approximation errors due to local linearizations and it avoids the problem of shifting locations of the origin of the local coordinate system. We analyse the variations of the Riemann metrics for different observers obtained from new color matching experiments and describe three variations of the basic method. The performance of the methods is evaluated with the help of semantic differential (SD) tests.