Using genetic algorithms for spectral-based printer characterization

In recent years, many methods have been proposed for the spectral-based characterization of inkjet printers. To our knowledge, the majority of these are based on a physical description of the printing process, employing different strategies to deal with mechanical dot gain and the physical interaction among inks. But our experience tells us that as printing is a physical process involving a large number of effects and unpredictable interactions, it is not unusual to be unable to fit a mathematical model to a given printer. The question becomes, therefore, whether it is feasible, and to what degree, to employ an analytical printer model even if it appears to be incapable of describing the behavior of a given device. A key objective of our work is to obtain a procedure that can spectrally characterize any printer, regardless of the paper and the printer driver used. We consider in fact the printers RGB devices, and incorporate the printer driver operations, even if they are unknown to us, into the analytical model. We report here our experimentation on the use of genetic algorithms to tune a spectral printer model based on the Yule-Nielsen modified Neugebauer equation. In our experiments we have considered three different inkjet printers and used different kinds of paper and printer drivers. For each device the printer model has been tuned, using a genetic algorithm, on a data set of some 150 measured reflectance spectra. The test set was composed of 777 samples, uniformly distributed in the RGB color space