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
Vishal.Monga@xeroxlabs.com -
geisler@mail.utexas.edu -
bevans@ece.utexas.edu
Paper -
Vector Diffusion Filters
Online
subjective testing -
Halftoning Research
at UT Austin
Abstract
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).
Images
Original
Halftone using Linearized
CIELab
Halftone using opponent
color space
Halftone using YUV
color space
Halftone using YIQ
color space
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