Improving image quality in multi-channel printing - multilevel halftoning, color separation and graininess characterization

Abstract

Color printing is traditionally achieved by separating an input image into four channels (CMYK) and binarizing them using halftoning algorithms, in order to designate the locations of ink droplet placement. Multi-channel printing means a reproduction that employs additional inks other than these four in order to augment the color gamut (scope of reproducible colors) and reduce undesirable ink droplet visibility, so-called graininess.One aim of this dissertation has been to characterize a print setup in which both the primary inks CMYK and their light versions are used.The presented approach groups the inks, forming subsets, each representing a channel that is reproduced with multiple inks. To halftone the separated channels in the present methodology, a specific multilevel halftoning algorithm is employed, halftoning each channel to multiple levels. This algorithm performs the binarization from the ink subsets to each separate colorant. Consequently, the print characterization complexity remains unaltered when employing the light inks, avoiding the normal increase in computational complexity, the one-to-many mapping problem and the increase in the number of training samples. The results show that the reproduction is visually improved in terms of graininess and detail enhancement.The secondary color inks RGB are added in multi-channel printing to increase the color gamut. Utilizing them, however, potentially increases v the perceived graininess. Moreover, employing the primary, secondary and light inks means a color separation from a three-channel CIELAB space into a multi-channel colorant space, resulting in colorimetric redundancy in which multiple ink combinations can reproduce the same target color. To address this, a proposed cost function is incorporated in the color separation approach, weighting selected factors that influence the reproduced image quality, i.e. graininess and color accuracy, in order to select the optimal ink combination. The perceived graininess is modeled by employing S-CIELAB, a spatial low-pass filtering mimicking the human visual system. By applying the filtering to a large dataset, a generalized prediction that quantifies the perceived graininess is carried out and incorporated as a criterion in the color separation.Consequently, the presented research increases the understanding of color reproduction and image quality in multi-channel printing, provides concrete solutions to challenges in the practical implementation, and rises the possibilities to fully utilize the potential in multi-channel printing for superior image quality.