# Chromatic Adaptation Transform by Spectral Reconstruction (Preprint)

## TL;DR

This paper introduces a novel chromatic adaptation transform based on spectral reconstruction, which operates reliably across all real color and illuminant pairs, surpassing existing models in robustness despite increased complexity.

## Contribution

The paper presents a new CAT that avoids the standard von Kries model, using spectral reconstruction to improve robustness and eliminate failure modes of existing CATs.

## Key findings

- Performs as well or better than recent CATs on datasets
- Immune to negative tristimulus values
- Operates reliably on all real color and illuminant pairs

## Abstract

A color appearance model (CAM) is an advanced colorimetric tool used to predict color appearance under a wide variety of viewing conditions. A chromatic adaptation transform (CAT) is an integral part of a CAM. Its role is to predict "corresponding colors," that is, a pair of colors that have the same color appearance when viewed under different illuminants, after partial or full adaptation to each illuminant. Modern CATs perform well when applied to a limited range of illuminant pairs and a limited range of source (test) colors. However, they can fail if operated outside these ranges. For imaging applications, it is important to have a CAT that can operate on any real color and illuminant pair without failure. This paper proposes a new CAT that does not operate on the standard von Kries model of adaptation. Instead it relies on spectral reconstruction and how these reconstructions behave with respect to different illuminants. It is demonstrated that the proposed CAT is immune to some of the limitations of existing CATs (such as producing colors with negative tristimulus values). The proposed CAT does not use established empirical corresponding-color datasets to optimize performance, as most modern CATs do, yet it performs as well as or better than the most recent CATs when tested against the corresponding-color datasets. This increase in robustness comes at the expense of additional complexity and computational effort. If robustness is of prime importance, then the proposed method may be justifiable.

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Source: https://tomesphere.com/paper/1902.10160