Generating test images and halftoning filters with co-evolutionary GA

Authors:
Timo Mantere
Affiliations:
Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland
Venue:
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Year:
2003

Citing 3
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Artificial evolution for computer graphics

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Abstract

In this paper, we evaluate the potentials of using co-evolutionary optimization method to automatically generate both halftoning filters for the image processing software and test images for them. One genetic algorithm generates halftone filters and at the same time, another genetic algorithm tries to create the hardest test image for each filter. The best filter being 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 respectively filter population defines the fitness of test image.