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Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector

M Y Ting1, E A Riskin

  • 1Dept. of Electr. Eng., Washington Univ., Seattle, WA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
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This study introduces a novel method for compressing binary error-diffused images by decoding them into grayscale using nonlinear filters. This approach enhances image quality compared to direct binary compression and offers low computational complexity.

Area of Science:

  • Image processing
  • Data compression
  • Computer vision

Background:

  • Error diffusion is a common technique for converting grayscale images to binary halftones.
  • Direct compression of binary error-diffused images often results in significant quality degradation.
  • Existing compression methods for binary images struggle to preserve visual fidelity.

Purpose of the Study:

  • To propose a novel method for compressing binary error-diffused images.
  • To improve the compression quality of error-diffused images.
  • To develop a computationally efficient compression technique applicable to various halftoning algorithms.

Main Methods:

  • The proposed method decodes binary error-diffused images into the grayscale domain using nonlinear filters.

Related Experiment Videos

  • The decoded grayscale images are then compressed.
  • The technique is designed to be compatible with any existing halftoning algorithm.

    • Main Results:

    • Compressing error-diffused images in the grayscale domain yields superior image quality compared to direct binary compression.
    • The method demonstrates low computational complexity, making it efficient for practical applications.
    • The approach is versatile and can be applied regardless of the specific halftoning algorithm used.

    Conclusions:

    • Decoding error-diffused images to grayscale before compression is an effective strategy for enhancing image quality.
    • The proposed nonlinear filtering approach offers a computationally efficient and versatile solution for binary image compression.
    • This method represents a significant improvement for applications requiring high-quality compressed binary images.