Digital Halftoning: Computational Intelligence-Based Approach

Authored by: Chatterjee Arpitam , Kanai Chra Paul , Tudu Bipan

Encyclopedia of Image Processing

Print publication date:  November  2018
Online publication date:  November  2018

Print ISBN: 9781482244908
eBook ISBN: 9781351032742
Adobe ISBN:

10.1201/9781351032742-140000166

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Abstract

Digital halftoning is a process of representing continuous tone (contone) images comprising many tonal variations with devices that can support limited number of tones at output. A very common example may be printing a grayscale image consisting of different variations of gray shades with a black-and-white printer that can print only one color, that is, black on white paper. Historically, halftoning was invented for printing; however, with the advent of different digital displays, this is an indispensable process for digital displays with a limited number of tonal reproducibility. Considering the grayscale image reproduction, this can be framed as an optimization problem where the solutions are the optimized halftone patterns of the subjected continuous tone images. This entry presents the application of computational intelligence (CI) toward generation of optimized halftone patterns through minimization of visual cost function. The fundamental framework of applying CI is elaborated with three popular CI algorithms, namely, genetic algorithm, particle swarm optimization, and artificial bee colony optimization. However, the applications of binary versions of them are being presented due to the nature of the problem. The results are portrayed in detail with different qualitative mathematical evaluations. The visual and objective evaluations of the results in comparison with the output of standard digital halftoning techniques show the possible potential of applying CI techniques for obtaining improved halftones.

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