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The eidochromatic transform for color-image coding.

Irina Popovici1, W Douglas Withers

  • 1Department of Mathematics, United States Naval Academy, Annapolis, MD 21402, USA. popovici@usna.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 15, 2005
PubMed
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A new eidochromatic transform improves lossy color image coding by reducing data redundancy across color and spatial dimensions. This method, when combined with JPEG 2000, significantly decreases file sizes for compressed images.

Area of Science:

  • Digital image processing
  • Information theory
  • Computer vision

Background:

  • Current image coding standards like JPEG 2000 employ color-component and spatial transforms to reduce data redundancy.
  • These transforms independently address color and spatial information, leaving potential for further optimization.

Purpose of the Study:

  • To introduce and evaluate the eidochromatic transform for enhanced lossy color image compression.
  • To explore a novel transform that simultaneously processes color and spatial image data to minimize redundancy.

Main Methods:

  • The eidochromatic transform is proposed as an additional step after existing color-component and spatial transforms.
  • This transform integrates information across color channels and spatial locations.
  • Performance was evaluated by measuring the reduction in static entropy of chrominance components and overall file size reduction when combined with JPEG 2000.

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Main Results:

  • The eidochromatic transform achieved reductions in the static entropy of chrominance components by up to 40% or more.
  • When integrated with JPEG 2000, the eidochromatic transform resulted in overall file size reductions of up to 27% for lossily compressed color images.

Conclusions:

  • The eidochromatic transform is an effective tool for improving lossy color image coding efficiency.
  • Simultaneous processing of color and spatial information offers significant advantages in data redundancy reduction.
  • This technique holds promise for developing more efficient image compression standards.