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Image coding based on a morphological representation of wavelet data.

S D Servetto1, K Ramchandran, M T Orchard

  • 1Dept. of Comput. Sci., Illinois Univ., Urbana, IL 61801, USA. servetto@cs.uiuc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This study introduces novel morphology-based image coding algorithms using wavelet coefficients. These methods offer high performance for embedded or fixed-rate coding and improve perceptual quality through joint compression and noise reduction.

Area of Science:

  • Digital Image Processing
  • Signal Processing
  • Computer Vision

Background:

  • Wavelet transforms are effective for image compression.
  • Morphological image processing offers shape analysis capabilities.
  • Current image coding methods often require explicit shape descriptors.

Purpose of the Study:

  • To investigate statistical properties of wavelet coefficients in image data.
  • To design morphology-based image coding algorithms leveraging these statistics.
  • To develop adaptable coders for embedded or fixed rates with improved perceptual quality.

Main Methods:

  • Experimental analysis of wavelet coefficient statistics.
  • Design of two morphology-based image coding algorithms.
  • Implicit encoding of shape information via wavelet coefficients.

Related Experiment Videos

  • Adaptation of quantizers for embedded or fixed-rate coding.

    • Main Results:

    • Algorithms achieve performance comparable to state-of-the-art zerotree-based methods.
    • Implicit shape information encoding reduces coding complexity and rate.
    • Proposed methods yield superior perceptual quality in reconstructed images.
    • Joint compression and noise reduction properties enhance image fidelity.

    Conclusions:

    • Morphology-based image coding using wavelet statistics is effective.
    • The proposed algorithms offer flexibility in rate control and high performance.
    • Implicit shape encoding is a viable alternative to explicit descriptors.
    • The algorithms demonstrate potential for improved image compression and quality.