Testing Digital Halftoning Filters by Generating Test Images and Filters Co-Evolutionarily

摘要

In this paper we evaluate the potential of using the co-evolutionary optimization method to automatically and concurrently generate halftoning filters and their test images. One genetic algorithm tries to generate the best halftone filters, while the other genetic algorithm tries to create the hardest test image for the filters. The best filter is the one for which the hardest test image, when dithered, differs least from the original. An image population defines the fitness of halftoning filters and vice versa.