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Adaptive scalar quantization without side information.

A Ortega1, M Vetterli

  • 1Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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This study presents a backward adaptive scalar quantization technique that estimates source distributions without side information. The method achieves near-optimal performance, minimizing adaptivity loss for image compression and similar applications.

Area of Science:

  • Digital Signal Processing
  • Information Theory
  • Data Compression

Background:

  • Adaptive scalar quantization is crucial for data compression when source statistics are unknown or change.
  • Existing methods often require transmitting side information to adapt quantizers.
  • Backward adaptive schemes offer a more efficient approach by utilizing previously quantized data.

Purpose of the Study:

  • To introduce a novel backward adaptive scalar quantization technique.
  • To enable adaptation to changing source statistics without requiring side information.
  • To improve compression efficiency in applications like image compression.

Main Methods:

  • The proposed adaptive quantizer is decomposed into model estimation and quantizer design blocks.

Related Experiment Videos

  • Nonparametric estimation techniques are employed to estimate the source probability density function (pdf).
  • The estimated pdf is used to redesign the quantizer using standard algorithms.

    • Main Results:

    • The scheme approximates a universal quantizer for smooth pdfs, with accuracy improving at higher rates.
    • Performance loss due to adaptivity is minimal in typical scenarios.
    • Achieved signal-to-noise ratios within 0.05 dB of optimal Lloyd-Max for memoryless sources and >1.5 dB gain over fixed quantizers for bimodal sources.

    Conclusions:

    • The introduced backward adaptive scalar quantization technique effectively adapts to changing source statistics.
    • The method offers a robust and efficient solution for data compression applications.
    • The approach demonstrates significant performance gains compared to fixed quantizers.