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Related Experiment Videos

Vector quantization of image subbands: a survey.

P C Cosman1, R M Gray, M Vetterli

  • 1Dept. of Electr. and Comput. Eng., California Univ., San Diego, La Jolla, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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Subband and wavelet decompositions, combined with vector quantization (VQ), offer efficient image coding by exploiting signal correlations and human visual system characteristics. This survey reviews VQ methods for subband/wavelet transform coefficients.

Area of Science:

  • Image processing
  • Signal processing
  • Computer vision

Background:

  • Subband and wavelet decompositions are crucial for image coding due to decorrelation, energy concentration, and multiresolution properties.
  • These techniques enable efficient coding by matching frequency bands to signal statistics and human visual perception.
  • Vector quantization (VQ) is a method for converting decomposed signals into bits, leveraging remaining correlations.

Purpose of the Study:

  • To survey and present various methods utilizing vector quantization (VQ) for subband and wavelet transform coefficients.
  • To highlight the advancements and applications of VQ in conjunction with transform coding techniques.

Main Methods:

  • Reviewing existing research on the application of VQ to subband and wavelet transform coefficients.

Related Experiment Videos

  • Analyzing the integration of VQ with transform coding for image compression.

    • Main Results:

    • VQ effectively exploits inter- and intraband correlations in decomposed image signals.
    • VQ allows for flexible partitioning of high-dimensional vector spaces for efficient coding.
    • Research since 1988 shows significant interest in VQ for subband/wavelet image coding.

    Conclusions:

    • The combination of subband/wavelet decomposition and VQ is a promising approach for advanced image coding.
    • VQ offers a powerful tool for optimizing bit allocation based on signal characteristics and visual perception.
    • Continued research in this area is expected to yield further improvements in image compression efficiency.