Multi-channel printing employs additional inks to improve the perceived image quality by reducing the graininess and augmenting the printer gamut. It also requires a color separation that deals with the one-to-many mapping problem imposed when using more than three inks. The proposed separation model incorporates a multilevel halftoning algorithm, reducing the complexity of the print characterization by grouping inks of similar hues in the same channel. In addition, a cost function is proposed that weights selected factors influencing the print and perceived image quality, namely color accuracy, graininess and ink consumption. The graininess perception is qualitatively assessed using S-CIELAB, a spatial low-pass filtering mimicking the human visual system. By applying it to a large set of samples, a generalized prediction quantifying the perceived graininess is carried out and incorporated as a criterion in the color separation. The results of the proposed model are compared with the separation giving the best colorimetric match, showing improvements in the perceived image quality in terms of graininess at a small cost of color accuracy and ink consumption. (c) 2016 Wiley Periodicals, Inc.