In the standard reflectance model for inhomogeneous materials it is assumed that light is reflected by two independent mechanisms. One component is reflected at the interface of the material and air. Light reflected by this mechanism does not interact with surface colorant, and its spectral composition is assumed to equal that of the incident light. The second component is reflected after entering and interacting with the subsurface structure of the material. This interaction substantially changes the spectral composition of the reflected light. We adopt a vector analysis technique for testing the standard reflectance model. Further, we develop a computational method to determine the components of the observed spectra, and we obtain an estimate of the illuminant without using a reference white standard. Finally, we evaluate the accuracy of the standard model and the feasibility of the illuminant spectral estimation by using several test objectives.

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Knowledge of the scene illuminant spectral power distribution is useful for many imaging applications, such as color image reproduction and automatic algorithms for image database applications. In many applications accurate spectral characterization of the illuminant is impossible because the input device acquires only three spectral samples. In such applications it is sensible to set a more limited objective of classifying the illuminant as belonging to one of several likely types. We describe a data set of natural images with measured illuminants for testing illuminant classification algorithms. One simple type of algorithm is described and evaluated by using the new data set. The empirical measurements show that illuminant information is more reliable in bright regions than in dark regions. Theoretical predictions of the algorithm's classification performance with respect to scene illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.

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