  It is an ill-posed problem to recover the true scene colors from a color
biased image by discounting the effects of scene illuminant and camera spectral
sensitivity (CSS) at the same time. Most color constancy (CC) models have been
designed to first estimate the illuminant color, which is then removed from the
color biased image to obtain an image taken under white light, without the
explicit consideration of CSS effect on CC. This paper first studies the CSS
effect on illuminant estimation arising in the inter-dataset-based CC
(inter-CC), i.e., training a CC model on one dataset and then testing on
another dataset captured by a distinct CSS. We show the clear degradation of
existing CC models for inter-CC application. Then a simple way is proposed to
overcome such degradation by first learning quickly a transform matrix between
the two distinct CSSs (CSS-1 and CSS-2). The learned matrix is then used to
convert the data (including the illuminant ground truth and the color biased
images) rendered under CSS-1 into CSS-2, and then train and apply the CC model
on the color biased images under CSS-2, without the need of burdensome
acquiring of training set under CSS-2. Extensive experiments on synthetic and
real images show that our method can clearly improve the inter-CC performance
for traditional CC algorithms. We suggest that by taking the CSS effect into
account, it is more likely to obtain the truly color constant images invariant
to the changes of both illuminant and camera sensors.
