IEEE Signal Processing Letters, vol. 10, no. 4, pp. 93-97, April 2003.

Linear, Color Separable, Human Visual System Models for Vector Error Diffusion Halftoning

Vishal Monga, Wilson S. Geisler, and Brian L. Evans

Center for Perceptual Systems, The University of Texas at Austin, Austin, TX 78712 USA - -

Paper - Vector Diffusion Filters

Online subjective testing - Halftoning Research at UT Austin


Image halftoning converts a high-resolution image to a low-resolution image, e.g. a 24-bit color image to a three-bit color image, for printing and display. Vector error diffusion captures correlation among color planes by using an error filter with matrix-valued coefficients. In optimizing vector error filters, Damera-Venkata and Evans transform the error image into an opponent color space where Euclidean distance has perceptual meaning. This paper evaluates color spaces for vector error filter optimization. In order of increasing quality, the color spaces are YIQ, YUV, opponent (by Poirson and Wandell), and linearized CIELab (by Flohr, Kolpatzik, Balasubramanian, Carrara, Bouman, and Allebach).



Halftone using Linearized CIELab

Halftone using opponent color space

Halftone using YUV color space

Halftone using YIQ color space

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Last Updated 01/16/06.