A simple and efficient error-diffusion algorithm

Abstract

In this contribution, we introduce a new error-diffusion scheme that produces higher quality results. The algorithm is faster than the universally used Floyd-Steinberg algorithm, while maintaining its original simplicity. The efficiency of our algorithm is based on a deliberately restricted choice of the distribution coefficients. Its pleasing nearly artifact-free behavior is due to the off-line minimization process applied to the basic algorithm's parameters (distribution coefficients). This minimization brings the Fourier spectra of the selected key intensity levels as close as possible to the corresponding "blue noise" spectra. The continuity of the algorithm's behavior across the full range of intensity levels is achieved thanks to smooth interpolation between the distribution coefficients corresponding to key levels. This algorithm is applicable in a wide range of computer graphics applications, where a color quantization algorithm with good visual properties is needed.

Dot-Diffused Halftoning With Improved Homogeneity

“…To this end, the perceived error was individually utilized with a HVS-like model for a homogenous halftone texture and better similarity to the tones of interest [12], [20]- [21]. In addition, the power spectrum density (PSD) is employed to measure whether the blue noise property is met [6]- [7], [21]- [23]. For this, Zhou and Fang [6] evaluated the correlations of the three various directions on PSD for a circle-shape spectrum, and the power concentricity was estimated through the power ratio between the entire spectrum and those under the cutoff frequency.…”

“…These methods and the related settings are defined as follows: 1) DMDBS [13] (abbr. : IT-DMDBS), 2) Ostromoukhov's ED [7] (abbr. : ED-Ost), and 3) Zhou-Fang's ED [6] (abbr.…”

Improved initialisation for centroidal Voronoi tessellation and optimal Delaunay triangulation

“…One approach is to use an error diffusion strategy [31]. However, this method produces an initial distribution which only approximately follows the prescribed density.…”

Dense Glyph Sampling for Visualization

“…Anisotropic spot samples with certain characteristics, such as spatially varying density and size, have many applications in visualization and computer graphics ranging from glyph rendering and texture generation for visualization purposes [14,8,13,11,12,3], digital halftoning [19,15,25] to mesh generation [2,16,22]. While some of the desirable properties of the samples are similar across applications, the goals and appropriate sampling strategies are problem-dependent.…”