On spatial quantization of color images

Image quantization and digital halftoning, two fundamental image processing problems, are generally performed sequentially. and, in most cases, indepe ndent of each other, Color reduction with a pixel-wise defined distortion m easure and the halftoning process with its local averaging neighborhood typ ically optimize different quality criteria or, frequently, follow a heurist ic approach without reference to any quantitative quality measure. In this paper, ne propose a new model to simultaneously quantize and halftone color images. The method is based on a rigorous cost-function approach which opt imizes a quality criterion derived from a simplified model of human percept ion. It incorporates spatial and contextual information into the quantizati on and thus overcomes the artificial separation of quantization and halfton ing, Optimization is performed by an efficient multiscale procedure which s ubstantially alleviates the computational burden. The quality criterion and the optimization algorithms are evaluated on a re presentative set of artificial and real-world images showing a significant image quality improvement compared to standard color reduction approaches. Applying the developed cost function, we also suggest a new distortion meas ure for evaluating the overall quality of color reduction schemes.