IEEE Transactions on Image Processing,
vol. 10, no. 1, pp. 104-116, Jan. 2001.
Adaptive Threshold Modulation for Error Diffusion Halftoning
Brian L. Evans
Department of Electrical and Computer Engineering,
Engineering Science Building,
The University of Texas at Austin,
Austin, TX 78712-1084 USA
Toolbox for Matlab -
at UT Austin
Grayscale digital image halftoning quantizes each pixel to one bit.
In error diffusion halftoning, the quantization error at each pixel
is filtered and fed back to the input in order to diffuse the
quantization error among the neighboring grayscale pixels.
Error diffusion introduces nonlinear distortion (directional artifacts),
linear distortion (sharpening), and additive noise.
Threshold modulation, which alters the quantizer input, has been previously
used to reduce either directional artifacts or linear distortion.
This paper presents an adaptive threshold modulation framework
to improve halftone quality by optimizing error diffusion parameters in
the least squares sense.
The framework models the quantizer implicitly, so a wide variety of
quantizers may be used.
Based on the framework, we derive adaptive algorithms to optimize
In edge enhancement halftoning, we minimize linear distortion by
controlling the sharpening control parameter.
We may also break up directional artifacts by replacing the thresholding
quantizer with a deterministic bit flipping (DBF) quantizer.
For green noise halftoning, we optimize the hysteresis coefficients.
- green noise halftoning, and
- edge enhancement halftoning.
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Last Updated 11/08/04.