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Advances in residual vector quantization: a review.

C F Barnes1, S A Rizvi, N M Nasrabadi

  • 1Georgia Tech. Res. Inst., Georgia Inst. of Technol., Atlanta, GA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study surveys advances in residual vector quantization (RVQ), detailing design techniques for efficient, variable-rate, and embedded coding systems. New successive approximation RVQ methods are introduced for image compression applications.

Area of Science:

  • Data Compression
  • Signal Processing
  • Information Theory

Background:

  • Residual Vector Quantization (RVQ) is a powerful technique for data compression.
  • Existing RVQ methods face challenges with large codebooks and differing encoder/decoder designs.
  • The need for efficient and adaptable compression algorithms is growing.

Purpose of the Study:

  • To survey recent advances in Residual Vector Quantization (RVQ).
  • To elaborate on design techniques for complex RVQ systems.
  • To introduce novel RVQ approaches for enhanced image compression.

Main Methods:

  • Survey of existing RVQ literature and techniques.
  • Discussion of joint encoder and decoder optimality.
  • Development and application of new successive approximation RVQ methods.

Related Experiment Videos

  • Integration with neural networks and wavelet transforms.

    • Main Results:

    • Elaboration of design techniques for RVQs with many stages and distinct codebooks.
    • Examination of fixed-rate and variable-rate RVQs with entropy coding.
    • Introduction of a new successive approximation RVQ with variable block rates.
    • Application to image waveforms and subbands using wavelet transforms.

    Conclusions:

    • RVQ technology has advanced significantly, offering improved compression.
    • New RVQ designs enable efficient, embedded, and refinable coding.
    • The proposed RVQ methods show promise for advanced image compression applications.