Kernel weights optimization for error diffusion halftoning method

Victor Fedoseev

doi:10.1117/12.2180540

14 February 2015 Kernel weights optimization for error diffusion halftoning method

Victor Fedoseev

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 944521 (2015) https://doi.org/10.1117/12.2180540
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

Abstract

This paper describes a study to find the best error diffusion kernel for digital halftoning under various restrictions on the number of non-zero kernel coefficients and their set of values. As an objective measure of quality, WSNR was used. The problem of multidimensional optimization was solved numerically using several well-known algorithms: Nelder– Mead, BFGS, and others. The study found a kernel function that provides a quality gain of about 5% in comparison with the best of the commonly used kernel introduced by Floyd and Steinberg. Other kernels obtained allow to significantly reduce the computational complexity of the halftoning process without reducing its quality.

Citation Download Citation

Victor Fedoseev "Kernel weights optimization for error diffusion halftoning method", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944521 (14 February 2015); https://doi.org/10.1117/12.2180540

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