Modeling, Measuring, and Compensating Color Weak Vision

Abstract

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.

Design of highly perceptible dual-resonance all-dielectric metasurface colorimetric sensor via deep neural networks

“…In other words, the smaller the ellipse size is, the easier it is to recognize even trivial color variations. According to the previous color perception experiments 38 , 39 , the region with the smallest ellipse locates is in the vicinity of the origin at the ( coordinate and between 40 and 60. These values correspond exactly to the area in which the calculated color of the target spectra is located as shown in Fig.…”