Partitioning schemes for use in a neural network for digital image halftoning

Jean R. S. Blair; Tommy D. Wagner; David A. Nash; Eugene K. Ressler Jr.; Barry L. Shoop; Timothy J. Talty

doi:10.1117/12.380594

30 March 2000 Partitioning schemes for use in a neural network for digital image halftoning

Jean R. S. Blair, Tommy D. Wagner, David A. Nash, Eugene K. Ressler Jr., Barry L. Shoop, Timothy J. Talty

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380594
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

In this research, we investigate partitioning schemes for reducing the computational complexity of an error diffusion neural network (EDN) for the application of digital halftoning. We show that by partitioning the original image into k subimages, the time required to perform the halftoning using an EDN is reduced by as much as a factor of k. Motivated by this potential speedup, we introduce three approaches to partitioning with varying degrees of overlap and communication between the partitions. We quantitatively demonstrate that the Constrained Framing approach produces halftoned images whose quality is as good as the quality of halftoned images produced by the EDN without partitioning.

Citation Download Citation

Jean R. S. Blair, Tommy D. Wagner, David A. Nash, Eugene K. Ressler Jr., Barry L. Shoop, and Timothy J. Talty "Partitioning schemes for use in a neural network for digital image halftoning", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380594

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