  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.
