Paper
1 May 1994 Blue noise and model-based halftoning
Mark A. Schulze, Thrasyvoulos N. Pappas
Author Affiliations +
Proceedings Volume 2179, Human Vision, Visual Processing, and Digital Display V; (1994) https://doi.org/10.1117/12.172669
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States
Abstract
`Model-based' halftoning techniques use models of visual perception and printing to produce high quality images using standard laser printers. Blue-noise screening is a dispersed-dot ordered dither technique that 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 least- squares model-based (LSMB) algorithm results in halftones inferior to those obtained with modified error diffusion (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 those 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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark A. Schulze and Thrasyvoulos N. Pappas "Blue noise and model-based halftoning", Proc. SPIE 2179, Human Vision, Visual Processing, and Digital Display V, (1 May 1994); https://doi.org/10.1117/12.172669
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Cited by 15 scholarly publications.
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KEYWORDS
Printing

Model-based design

Eye models

Diffusion

Halftones

Visual process modeling

Visualization

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