Data-driven spectral model for color gamut simulation

We present a new method to estimate color gamut from primary color spectra only. By using this method, the number of prints and measurements for printer characterization is greatly reduced, which is of particular importance if there are many ink candidates to be combined. The method consists in building a mapping between primary color spectra and their combinations, based on a training set. The mapping is data-driven, without relying on assumptions based on classic theoretical models. Furthermore, we study the effects of smoothing this mapping and its consequences in color estimation accuracy. We tested the method on recently released Latex-based ink-jet ink, for different pigment and latex loads. The mean simulation accuracy error, optimizing the smoothing parameter, was below 1.5 dE, and gamut estimation error below 2% error. For pigment kinds, mean accuracy was below 4 dE, and gamut estimation error about 5%. In all cases, this new method outperforms other overprint estimation methods, such as Kubelka-Munk.