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Related Experiment Video

Updated: Jul 7, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
11:57

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

Modeling and quality assessment of halftoning by error diffusion.

T D Kite1, B L Evans, A C Bovik

  • 1Audio Precision, Inc., Beaverton, OR 97075, USA. tomk@ap.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
Summary

This study linearizes digital halftoning error diffusion algorithms. We developed objective measures for edge sharpening and noise shaping to evaluate halftone quality.

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Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Digital halftoning converts grayscale images to one-bit pixels.
  • Error diffusion algorithms reduce quantization error via feedback filtering.
  • Existing methods lack objective quality assessment for error diffusion.

Purpose of the Study:

  • To linearize error diffusion algorithms for quantitative analysis.
  • To develop objective measures for key halftoning artifacts: edge sharpening and noise shaping.
  • To evaluate the subjective quality impact of these artifacts.

Main Methods:

  • Modeling the quantizer as linear gain plus additive noise.
  • Confirming the linear model's accuracy through three independent methods.

Related Experiment Videos

Last Updated: Jul 7, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
11:57

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

  • Quantifying edge sharpening using linear gain and developing a formula for gain estimation.
  • Quantifying noise shaping using a perceptually weighted signal-to-noise ratio on image residuals.
  • Calculating tonality using a measure similar to total harmonic distortion.

    • Main Results:

    • The linear model accurately represents error diffusion behavior.
    • Edge sharpening is directly proportional to the linear gain of the quantizer.
    • Objective measures for edge sharpening, noise shaping, and tonality were successfully developed.
    • The developed measures correlate with subjective halftone quality.

    Conclusions:

    • Linearizing error diffusion enables quantitative analysis of halftoning artifacts.
    • The proposed objective measures provide a reliable method for evaluating halftone quality.
    • This work facilitates the development and optimization of advanced error diffusion algorithms.