Entropy-constrained halftoning using multipath tree coding

We suggest an optimization-based method for halftoning that involves looking ahead before a decision for each binary output pixel is made. We first define a mixture distortion criterion that is a combination of a frequency-weighted mean square error (MSE) and a measure depending on the distances between minority pixels in the halftone. A tree-coding approach with the ML-algorithm is used for minimizing the distortion criterion to generate a halftone. While this approach generates halftones of high quality, these halftones are not very amenable to lossless compression. We introduce an entropy constraint into the cost function of the tree-coding algorithm that optimally trades off between image quality and compression performance in the output halftones