Uniform color spaces and natural image statistics

Many aspects of visual coding have been successfully predicted by starting from the statistics of natural scenes and then asking how the stimulus could be efficiently represented. We started from the representation of color char-acterized by uniform color spaces, and then asked what type of color environment they implied. These spaces are designed to represent equal perceptual differences in color discrimination or appearance by equal distances in the space. The relative sensitivity to different axes within the space might therefore reflect the gamut of colors in natural scenes. To examine this, we projected perceptually uniform distributions within the Munsell, CIE Luv or CIE Lab spaces into cone-opponent space. All were elongated along a bluish-yellowish axis reflecting covarying signals along the L–M and S–LM cardinal axes, a pattern typical (though not identical) to many natural environments. In turn, color distributions from environments were more uniform when projected into the CIE Lab perceptual space than when represented in a normalized cone-opponent space. These analyses suggest the bluish-yellowish bias in environmental colors might be an important factor shaping chromatic sensi-tivity, and also suggest that perceptually uniform color metrics could be derived from natural scene statistics and potentially tailored to specific environments. © 2012 Optical Society of America OCIS codes: 330.1690, 330.1720, 330.5020, 330.7310. 1