Pau Soler

Efficient color printer characterization based on extended Neugebauer spectral models

In order to print accurate colors on different substrates, color profiles must be created for each specific ink-media combination. We tackled the problem of creating such color profiles from only few color samples, in order to reduce the needed time of operation. Our strategy is to use a spectral reflectance prediction model in order to estimate a large sampling target (e.g. IT8.7/3) from only a small subset of color patches. In particular, we focused on the so-called Yule-Nielsen modified Spectral Neugebauer model, proposing new area coverage estimation, and a prediction of Neugebauer primaries, which can not be directly measured due to ink limiting. We reviewed the basis of such model, interpret it under the perspective of generalized averaging, and derived expressions to decouple optical and mechanical dot gain effects. The proposed area coverage estimations are based on assumptions of the printing process, and characterized through few extra color samples. We tested the models with thermal ink-jet printers on a variety of media, with dye-based and pigment-based inks. The IT8.7/3 target was predicted from 44 samples, with color average accuracy below 4 dE and maximum error below 8 dE, for dye-based inks, which performed better than pigment-based inks.

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.

Inter-substrate warping to predict color from reduced sample sets

In this paper we present a method to characterize the printer color output with few samples. Color measurements previously obtained on other substrates and stored in the printer are used to increase the accuracy of measurements in a new target media, thus reducing the number of samples needed. The method is simple and generic; a geometrical warp is applied to the color space to adapt the differences between the two media. The warping is built with a small set of measurements on the target media and extended to the entire color space with bi-harmonic spline interpolation. We tested the method on a HP T1100 ink-jet printer, at different levels of sampling -from 27 to 512 points- and with seven different substrate families covering a wide variety of applications. For 125 samples, results show a mean estimation error across media of mean 0.67 dE76 and 95 percentile 1.48 dE76 with respect to the finer sampling of 512 samples. This represents an improvement in color accuracy with respect to linear interpolation of about 60%, a relationship holds at other levels of sampling. In conclusion, color space warping is proven to be an effective method to reduce the needed color samples by using previously characterized media.