Physical measurements of surfaces' color-causing properties are typically spectroradiometric, whereas color-differencing comparisons are typically colormetric ones performed in some 3-D color space. In general, this downprojection of high-dimensional spectral data into some 3-dimensional color space incurs a loss of information, a loss that could be more critical in one color space than in another. One ecologically valid way of assessing the extent of this information loss is to determine how likely it is that a pair of surfaces which have distinctly different spectral properties would be colorimetrically indistinguishable. We describe a virtual ideal color-difference detector which uses standard color-difference metrics but has access to the absolute spectral difference in the color signals of the surface pair. Only when this ideal detector classes a surface pair as "different" yet a standard color-difference detector classes them as "same" is the pair said to be metameric. This paradigm is applied to a dataset of hyperspectral natural images using a wide variety of 3-D color spaces. The results show that, around thresholds which approximate human performance, the overal metamerism rate is very low, yet most pixels in an image will be metameric with at least one other image pixel. Thus, downprojecting spectral data onto a 3-D color space may compromise color discriminability, but is unlikely to affect color categorization performance, a finding which is in accord with evolutionary theories regarding the function of human color vision.