To what extent do observers' judgments of surface color with
natural scenes depend on global image statistics? To address this
question, a psychophysical experiment was performed in which images of
natural scenes under two successive daylights were presented on a
computer-controlled high-resolution color monitor. Observers reported
whether there was a change in reflectance of a test surface in the scene.
The scenes were obtained with a hyperspectral imaging system and included
variously trees, shrubs, grasses, ferns, flowers, rocks, and buildings.
Discrimination performance, quantified on a scale of 0 to 1 with a
color-constancy index, varied from 0.69 to 0.97 over 21 scenes and two
illuminant changes, from a correlated color temperature of 25,000 K to
6700 K and from 4000 K to 6700 K. The best account of these effects was
provided by receptor-based rather than colorimetric properties of the
images. Thus, in a linear regression, 43% of the variance in constancy
index was explained by the log of the mean relative deviation in spatial
cone-excitation ratios evaluated globally across the two images of a
scene. A further 20% was explained by including the mean chroma of the
first image and its difference from that of the second image and a further
7% by the mean difference in hue. Together, all four global color
properties accounted for 70% of the variance and provided a good fit to
the effects of scene and of illuminant change on color constancy, and,
additionally, of changing test-surface position. By contrast, a
spatial-frequency analysis of the images showed that the gradient of the
luminance amplitude spectrum accounted for only 5% of the variance.