LOCAL AND ISOTROPIC ERROR DIFFUSION: WHY AND HOW?

Because binary representation of images fit well VLSI constraints, the bilevel rendition of continuous tone pictures, referred to as the halftoning problem, is likely to play soon an important role in robot vision. In the present paper, two up-to-date techniques are analysed and compared in the frequential domain, particularly with respect to the “blue noise” concept as introduced in [1]. Disagreeing with previouly presented work [1], the standard “error diffusion” technique [2] is shown both theoretically and experimentally to be a poor blue noise generator, even when randomized [1]. Fortunately, we have been able to re-express the error diffusion concept as the minimization of a frequency-weighted mean squared error (FW-MSE) between the original image and its halftoned version [3]. Under this form, the error propagation scheme turns both isotropic and local, allowing the generation of real blue noise. We have called our method “neural halftoning” [4] due to peculiar connexionnist implementation properties.