Blue Noise and Model-Based Halftoning

Mark A. Schulze

The University of Texas at Austin

Thrasyvoulos N. Pappas

AT&T Bell Laboratories
Murray Hill, New Jersey

Presented at the SPIE Conference on Human Vision, Visual Processing, and Digital Display V

San Jose, California
6-10 February 1994


Complete reference:
M. A. Schulze and T. N. Pappas. "Blue noise and model-based halftoning." In B. E. Rogowitz and J. P. Allebach, eds., Human Vision, Visual Processing, and Digital Display V. Proc. SPIE, v. 2179 (1994) pp. 182-194. (San Jose, California, Feb. 6-10, 1994.)

"Model-based" halftoning techniques use models of visual perception and printing to produce high quality images using standard laser printers. Two such model-based techniques are the modified error diffusion (MED) and the least-squares model-based (LSMB) algorithms. Both produce excellent halftones, but require much more computation than conventional screening techniques.

Blue-noise screening is a dispersed-dot ordered dither technique which attempts to approximate the performance of error diffusion with much faster execution time. We use printer and visual system models to improve the design of blue-noise screens using the "void-and-cluster" method. We show that, even with these improvements, the performance of blue-noise screens does not match that of the model-based techniques.

We show that using blue-noise screened images as the starting point of the LSMB algorithm results in halftones inferior to those obtained with MED starting points. We also use simulated annealing to try to find the global optimum of the least-squares problem. Images found this way do not have significantly lower error than that resulting from the simple iterative LSMB technique starting with MED. This result indicates that the simple iterative LSMB algorithm with a MED starting point yields a solution close to the globally optimal solution of the least squares problem.

© Copyright 1994.

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Last Updated: 17 July 2003