ERROR-DIFFUSED IMAGE COMPRESSION USING A BINARY-TO-GRAY-SCALE DECODERAND PREDICTIVE PRUNED TREE-STRUCTURED VECTOR QUANTIZATION

This correspondence considers data compression of binary error diffuse d images. The original contribution is using nonlinear filters to deco de error-diffused images to compress them in the gray-scale domain; th is gives better image quality than directly compressing the binary ima ges. Our method is of low computational complexity and can work with a ny halftoning algorithm.