Jan Morovic

HANS: Controlling Ink-Jet Print Attributes Via Neugebauer Primary Area Coverages

Ink-jet print attributes such as color gamut, grain, and cost are consequences of the materials and printing technology used and of choices made during color management, color separation, and halftoning operation. Traditionally, color separation determines what amounts of the available inks to use for each reproducible color, and halftoning deals with the spatial distribution of inks that also results in the nature of their overprinting. However, using an ink space as a means of communication between color separation and halftoning gives access only to some of the printed patterns that a printing system is capable of and, therefore, only to a reduced range of print attributes. Here, a method, i.e., Halftone Area Neugebauer Separation, is proposed to gain access to all possible printable patterns by specifying relative area coverages of a printing systems Neugebauer primaries instead of only ink amounts. This results in delivering prints with more optimal attributes (e.g., using less ink and giving rise to a larger color gamut) than is possible using current methods.

Sampling Optimization for Printer Characterization by Greedy Search

Printer color characterization, e.g., in the form of an ICC output profile or other proprietary mechanism linking printer RGB/CMYK inputs to resulting colorimetry, is fundamental to a printing system delivering output that is acceptable to its recipients. Due to the inherently nonlinear and complex relationship between a printing systems inputs and the resulting color output, color characterization typically requires a large sample of printer inputs (e.g., RGB/CMYK) and corresponding color measurements of printed output. Simple sampling techniques here lead to inefficiency and a low return for increases in sampling density. While effective solutions have been proposed to this problem very recently, they either do not exploit the full possibilities of the 3-D/4-D space being sampled or they make assumptions about the underlying relationship being sampled. The approach presented here does not make assumptions beyond those inherent in the subsequent tessellation and interpolation applied to the resulting samples. Instead, the tradeoff here is the great computational cost of the initial optimization, which, however, only needs to be performed during the printing systems engineering and is transparent to its end users. Results show a significant reduction in the number of samples needed to match a given level of color accuracy.

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.