Abstract An accurate colorimetric characterization of digital still cameras (DSCs) is vital to any high-quality color-reproduction system. However, achieving a perfect relationship between DSC responses and input spectral radiance is not practically easy, even when they have
a reasonable linear relationship. In this research, we investigated differences in capturing geometries as a source of nonlinearity in camera characterization workflows. This nonlinearity can be corrected using a physical model describing the spectrophotometric changes according to illumination/capturing
geometries. We introduced a model based on the Saunderson equation as an approach to predict surface properties suitable for paint layers in different geometries. According to the results, the Saunderson surface correction successfully compensated for the dissimilarities among spectrophotometric
and spectroradiometric measurements, regardless of the capturing and lighting geometries. The model was also used for characterizing digital still cameras using matte, semi-glossy and glossy color targets as training datasets. The Saunderson-based models improved the transformation matrix
for different geometries compared to conventional methods. Also, the results confirmed the validity of a simpler derivation of the Saunderson surface correction based on linear matrix operations.