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adaptive threshold modulation for error diffusion halftoning

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Niranjan Damera-Venkata (HP Labs) and Dr. Thomas D. Kite (Audio Precision) Graduate Students: Mr. Vishal Monga Other Collaborators: http://users.ece.utexas.edu/~bevans/projects/halftoning/talks/ErrorDiffusion.html 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 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 or adaptive threshold an 8-bit grayscale image to a binary image, for printing and display. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to the input in order to diffuse the error among neighboring grayscale pixels. Grayscale error diffusion introduces nonlinear distortion (directional artifacts adaptive threshold modulation and false textures), linear distortion (sharpening), and additive noise. We describe approaches to compensate linear and nonlinear distortion based on observation that error diffusion is 2-D sigma-delta modulation (Anastassiou, 1989). 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 enhanceme

 

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