Metamer density estimated color correction

Color correction is the transformation of response values of scanners or digital cameras into a device- independent color space. In general, the transformation is not unique due to different acquisition and viewing illuminants and non-satisfaction of the Luther–Ives condition by a majority of devices. In this paper we propose a method that approximates the optimal color correction in the sense of a minimal mean error. The method is based on a representative set of reflectance spectra that is used to calculate a special basic collection of device metameric blacks and an appropriate fundamental metamer for each sensor response. Combining the fundamental metamer and the basic collection results in a set of reflectances that follows the density distribution of metameric reflectances if calculated by Bayesian inference. Transforming only positive and bounded spectra of the set into an observer’s perceptually uniform color space results in a point cloud that follows the density distribution of device metamers within the metamer mismatch space of acqcuisition system and human observer. The mean value of this set is selected for color correction, since this is the point with a minimal mean color distance to all other points in the cloud. We present the results of various simulation experiments considering different acquisition and viewing illuminants, sensor types, noise levels, and existing methods for comparison.