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Neugebauer Models for Color Error Diffusion Halftoning.

Kaiming Wu¹, Kohei Inoue¹, Kenji Hara¹

¹Department of Communication Design Science, Kyushu University, Fukuoka 815-8540, Japan.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This study introduces novel color halftoning methods using error diffusion and Neugebauer models. The proposed techniques better preserve image color and reduce noise compared to existing approaches.

Keywords:

error diffusion halftoning sparse Neugebauer model

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Area of Science:

Digital Image Processing
Color Science
Computer Vision

Background:

Halftoning is crucial for displaying continuous-tone images on devices with limited color palettes.
Conventional color halftoning methods often struggle to accurately reproduce original image colors.
Neugebauer models provide a framework for predicting color appearance based on halftone dot patterns.

Purpose of the Study:

To develop advanced color halftoning methods.
To improve color reproduction and reduce visual artifacts in halftone images.
To extend grayscale error diffusion techniques to color image halftoning.

Main Methods:

Reformulating grayscale error diffusion using a one-dimensional Neugebauer model.
Developing a color error diffusion method based on a three-dimensional Neugebauer model in RGB color space.
Proposing a sparse Neugebauer model derived from the minimal brightness variation criterion (MBVC) for error diffusion.

Main Results:

The proposed color halftoning methods demonstrate superior preservation of color content compared to conventional techniques.
The sparse Neugebauer model-based method effectively reduces halftone noise.
Experimental results validate the enhanced color fidelity and noise reduction capabilities.

Conclusions:

The developed color error diffusion techniques offer improved performance in halftoning natural images.
The sparse Neugebauer model-based approach represents a significant advancement in reducing halftone noise.
These methods contribute to better visual quality in digital color imaging.