Green Noise Digital Halftoning With Multiscale Error Diffusion

• Published in: IEEE transactions on image processing Vol. 19; no. 7; pp. 1808 - 1823
• Main Authors: FUNG, Yik-Hing, CHAN, Yuk-Hee
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Clustering algorithms; Clusters; Detection, estimation, filtering, equalization, prediction; Diffusion; Error diffusion; Errors; Exact sciences and technology; Filters; Frequency modulation; green noise; halftoning; Hysteresis; Image analysis; Image processing; Image quality; Information, signal and communications theory; Multi-stage noise shaping; multiscale error diffusion (MED); multiscale processing; Noise; Preserves; Printing; Shape; Signal and communications theory; Signal processing; Signal processing algorithms; Signal, noise; Telecommunications and information theory; Performance evaluation; Printing; halftoning; Grey level image; Image processing; Dithering; green noise; multiscale processing; Digital processing; Artefact; Algorithm; Image quality; Simulation; multiscale error diffusion (MED); Multiscale method; Isotropic diffusion; Error diffusion
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2010.2044961

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithms as it can eliminate directional hysteresis completely and possesses a good blue noise characteristic. However, due to its filter design, it is not suitable for systems with poor isolated dot generation and instable dot gain. In this paper, we propose a MED algorithm to produce halftones of desirable green noise characteristics. This algorithm allows one to adjust the desirable cluster size freely through a single parameter and supports a linear relationship between the cluster size and the input gray level. With a close-to-isotropic diffusion filter, the algorithm can effectively remove pattern artifacts, eliminate directional artifacts and preserve original image details. Analysis and simulation results show that it provides better performance in terms of various aspects including dot distribution, anisotropy and output image quality as compared with other conventional green noise error diffusion algorithms.