Prof. Brian L. Evans
Department of Electrical and Computer Engineering
The University of Texas at Austin
Graduated Ph.D. Students: Dr. Niranjan Damera-Venkata (HP Labs) and Dr. Thomas D. Kite (Audio Precision)
Graduate Students: Mr. Vishal Monga
Other Collaborators: Prof. Alan C. Bovik (UT Austin) and Prof. Wilson S. Geisler (UT Austin)
Talk in Powerpoint and PDF formats
Halftoning Research at UT Austin - Halftoning Toolbox
Following the 1-D sigma-delta work of Ardalan and Paulos (1988), we replace the thresholding quantizer with a scalar gain plus additive noise. The amount of sharpening is proportional to the scalar gain. Setting the sharpness control parameter in the threshold modulation approach of Eschbach and Knox (1991) can theoretically eliminate sharpening effects. We use unsharpened halftones in perceptually weighted SNR measures. We also use the sharpness control parameter to achieve rate-distortion tradeoffs in JBIG2 compression of error diffused halftones.
We generalize the approach for linear distortion compensation by using an adaptive threshold modulation framework. Using the framework, we adaptively optimize the hysteresis coefficients in green noise error diffusion of Levien (1993). For edge enhancement halftoning, we minimize linear distortion by adapting the sharpness control parameter. We break up directional artifacts by using a deterministic bit flipping quantizer, which was used by Magrath and Sandler (1997) in sigma-delta research.
Finally, we generalize our work to vector error diffusion (Haneishi et al. 1993) for color images. The scalar gain becomes a matrix gain. We apply an adaptive framework to optimize visual quality by using a linear color vision model. We evaluate four linear color vision models via subjective testing.
We recently released a freely distributable halftoning toolbox for Matlab.