Abstract

The spectral dependence of the colorimetric characterisation of a typical scanner was investigated.
Different colour sets, including ColorChecker SG, Kodak Q-60 colour input target, a set of plain woven
coloured fabrics with a large colour gamut, and randomly selected samples of the Munsell Book of Color
were used as training and testing sets in the colorimetric characterisation of a scanner by employing a
non-linear regression method. The coefficient matrices were optimised for each particular media in the
training stage and used to predict the device-independent colorimetric data, i.e. CIELab values of other
media from their corresponding RGB values measured by the scanner. In order to extract the differences
between the applied sets and determine the actual dimensions of their reflectance spectra, the principal
component analysis technique was employed. As expected, it was observed that the different sets
benefit from diverse dimensional properties and, in some cases, the spectral behaviours of the first few
eigenvectors were apparently different. It was demonstrated that scanner colorimetric characterisation
depended on the spectral properties of the applied colour set in the training stage and, consequently,
the testing errors increased with increasing the spectral dissimilarity between the sets that were used in
training and testing sequences. It was concluded that, to achieve better colour reproduction results, the
scanner should be characterised for each media with specific spectral properties.