Testing digital halftoning filters by generating test images and filters coevolutionarily

Timo J. Mantere; Jarmo T. Alander

doi:10.1117/12.514704

30 September 2003 Testing digital halftoning software by generating test images and filters coevolutionarily

Timo J. Mantere, Jarmo T. Alander

Proceedings Volume 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision; (2003) https://doi.org/10.1117/12.514704
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States

Abstract

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.

Citation Download Citation

Timo J. Mantere and Jarmo T. Alander "Testing digital halftoning software by generating test images and filters coevolutionarily", Proc. SPIE 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, (30 September 2003); https://doi.org/10.1117/12.514704

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