If surfaces in a scene are to be distinguished by their color, their
neural representation at some level should ideally vary little with the
color of the illumination. Four possible neural codes were considered:
von-Kries-scaled cone responses from single points in a scene, spatial
ratios of cone responses produced by light reflected from pairs of
points, and these quantities obtained with sharpened (opponent-cone)
responses. The effectiveness of these codes in identifying surfaces was
quantified by information-theoretic measures. Data were drawn from a
sample of 25 rural and urban scenes imaged with a hyperspectral camera,
which provided estimates of surface reflectance at 10-nm intervals at
each of 1344 × 1024 pixels for each scene. In computer
simulations, scenes were illuminated separately by daylights of
correlated color temperatures 4000 K, 6500 K, and 25,000 K. Points were
sampled randomly in each scene and identified according to each of the
codes. It was found that the maximum information preserved under
illuminant changes varied with the code, but for a particular code it
was remarkably stable across the different scenes. The standard
deviation over the 25 scenes was, on average, approximately 1 bit,
suggesting that the neural coding of surface color can be optimized
independent of location for any particular range of illuminants.