In color management applications, it is essential to know the color responses of observers to arbitrary spectral radiances so that objective colorimetric quantities can be determined for use in quantitative color-matching applications. These spectral responses are typically transformed to color matching functions (CMFs) such as for the average CIE standard observer which is commonly used for the computation of various colorimetric, perceptual, and appearance attributes. While the standard CIE CMFs for the average observer have been extremely useful for this purpose, it is well-known that there is significant variation in the spectral response amongst color-normal observers. For color-critical applications, there is widespread interest in determining individual-observer color matching functions with minimal knowledge of field-of-view, age, state-of-adaptation, and other viewing conditions in the actual use-setting. By combining eigenvector analysis of CMF datasets with simple individualobserver metameric color matching exercises and multidimensional reconstruction, individual-observer CMFs can be predicted, transformed, and profiled for color-managed workflow.
FM halftoning is increasingly popular with traditional analog offset lithographic printing processes. There is a desire to offer this capability with digital presses based on electrophotographic printing (EP) technologies. However, the inherent instability of the EP process challenges the achievement of satisfactory print quality with dispersed-dot, aperiodic halftoning. The direct binary search (DBS) algorithm is widely considered to represent the gold standard of dispersed-dot, aperiodic halftone image quality. In this paper, we continue our previous efforts to adapt DBS to use with the Indigo liquid EP printing technology. We describe a complete color management pipeline for halftoning with a PARAWACS matrix designed using DBS. For the first time, we show actual printed patches obtained using our process. Our gamut mapping is performed in the YyCxCz color space, and is image-dependent. It incorporates several stages of alignment between the input and output spaces, as well as several stages of compression. After the gamut mapping, we tessellate the output color space into six global tetrahedra that each share the neutral axis, as an edge. Then, we determine the Neugebauer Primary Area Coverage (NPAC) for each pixel in the image to be printed by tetrahedral interpolation from the four nearest neighbors in the inverse printer mapping table. These four nearest neighbors are chosen so that only four Neugebauer primaries are used to render each pixel.