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Quantization of accumulated diffused errors in error diffusion.

Ti-Chiun Chang1, Jan P Allebach

  • 1Siemens Corporate Research, Inc., Princeton, NJ 08540, USA. ti-chiun.chang@siemens.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 24, 2005
PubMed
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Quantizing accumulated diffused error (ADE) in error diffusion halftoning reduces hardware costs. Feature-dependent quantizers offer improved image quality at lower bit rates, matching standard quantization levels.

Area of Science:

  • Digital image processing
  • Computer vision
  • Halftoning algorithms

Background:

  • Error diffusion is a popular halftoning technique for inkjet printers due to its image quality and moderate complexity.
  • Its serial nature necessitates buffering accumulated diffused error (ADE) data, posing hardware cost challenges for on-chip implementation.

Purpose of the Study:

  • To investigate the quantization of ADE to reduce storage requirements for hardware implementations of error diffusion.
  • To develop novel feature-dependent quantizers that improve image quality at reduced bit rates.

Main Methods:

  • Examined uniform and nonuniform quantizers for ADE data.
  • Proposed novel feature-dependent quantizers building on tone-dependency in error diffusion.
  • Optimized quantizer design using the Lloyd-Max algorithm and a training framework for tone-dependent error diffusion.

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Main Results:

  • 4-bit uniform quantization of ADE achieved halftone quality comparable to unquantized error diffusion.
  • Feature-dependent quantizers achieved quality similar to 4-bit uniform quantization at 2-3 bits per pixel.

Conclusions:

  • Quantization of ADE is an effective method to reduce hardware costs in error diffusion halftoning.
  • Feature-dependent quantizers offer a promising approach for high-quality, low-bit-rate halftoning.