Model-based halftoning of color images

We present a new class of models for color printers. They form the basis for model-based techniques that exploit the characteristics of the printer and the human visual system to maximize the quality of the printed images. We present two model-based techniques, the modified error diffusion (MED) algorithm and the least-squares model-based (LSMB) algorithm. Both techniques are extensions of the gray-scale model-based techniques and produce images with high spatial resolution and visually pleasant textures. We also examine the use of printer models for designing blue-noise screens. The printer models can account for a variety of printer characteristics. We propose a specific printer model that accounts for overlap between neighboring dots of ink and the spectral absorption properties of the inks. We show that when we assume a simple “oneminus- RGB” relationship between the red, green, and blue image specification and the corresponding cyan, magenta, and yellow inks, the algorithms are separable. Otherwise, the algorithms are not separable and the modified error diffusion may be unstable. The experimental results consider the separable algorithms that produce high-quality images for applications where the exact colorimetric reproduction of color is not necessary. They are computationally simple and robust to errors in color registration, but the colors are device dependent.