Pareto-optimal formulations for cost versus colorimetric accuracy trade-offs in printer color management

Abstract

Color management for the printing of digital images is a challenging task, due primarily to nonlinear ink-mixing behavior and the presence of redundant solutions for print devices with more than three inks. Algorithms for the conversion of image data to printer-specific format are typically designed to achieve a single predetermined rendering intent, such as colorimetric accuracy. In the present paper we present two CIELAB to CMYK color conversion schemes based on a general Pareto-optimal formulation for printer color management. The schemes operate using a 149-color characterization data set selected to efficiently capture the entire CMYK gamut. The first scheme uses artificial neural networks as transfer functions between the CIELAB and CMYK spaces. The second scheme is based on a reformulation of tetrahedral interpolation as an optimization problem. Characterization data are divided into tetrahedra for the interpolation-based approach using the program Qhull, which removes the common restriction that characterization data be well organized. Both schemes offer user control over trade-off problems such as cost versus reproduction accuracy, allowing for user-specified print objectives and the use of constraints such as maximum allowable ink and maximum allowable ΔE * ab . A formulation for minimization of ink is shown to be particularly favorable, integrating both clipping and gamut compression features into a single methodology. Codes developed as applications of these schemes were used to convert several CIELAB Tiff images to CMYK format, providing both qualitative and quantitative verification of the Pareto-optimal approach. Prints of the MacBeth ColorChecker tm chart were accurate within approximately to 3 ΔE * ab for in-gamut colors. Modifications to this approach are presented that offer user control over grey component replacement and provide additional options for rendering intent.