Printer model and least-squares halftoning using genetic algorithms

In this paper, a least-square model-based halftoning technique using a genetic algorithm is proposed to produce a halftone image by minimizing the perceived reflectance difference between the halftone image and its original image. We use a least-square criterion incorporating with the property of the human visual system to measure the difference between the two images. The genetic algorithm is used for investigating the complicated search problem. The standard halftoning techniques, such as error diffusion and least-square halftoning, produce gray-level distortion because of dot-gain problem. In this study, we use a modified dot-overlap printer model to compensate the gray-level distortion. The printer model combines with a measurement-based algorithm to estimate the print-dot radius and makes the proposed halftoning approach adapted to a wide variety of printers and papers. Experiments show that the proposed approach produces more accurate gray levels than several common-used halftoning methods produce.