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Discrete minimum entropy quantization.

M J Gormish1, J T Gill

  • 1Dept. of Electr. Eng., Stanford Univ., CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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We developed a method for minimum entropy quantization of signals, enabling more efficient data compression for images and sound. This technique optimizes signal representation by minimizing entropy under a distortion constraint.

Area of Science:

  • Information Theory
  • Signal Processing
  • Data Compression

Background:

  • Quantization is a fundamental process in digital signal processing.
  • Efficient data compression is crucial for managing large datasets in multimedia applications.
  • Entropy plays a key role in theoretical limits of data compression.

Purpose of the Study:

  • To establish a lower bound on the entropy of a quantized signal.
  • To develop a method for minimum entropy quantization (MEQ).
  • To enable more efficient compression of images and sound using variable-order models.

Main Methods:

  • Derivation of a simple lower bound for quantized signal entropy.
  • Development of an optimization method to find MEQ under a maximum distortion constraint.

Related Experiment Videos

  • Application to discrete memoryless random processes.

    • Main Results:

    • A novel lower bound on quantized signal entropy was established.
    • A practical method for determining minimum entropy quantization was presented.
    • The proposed quantization scheme enhances the efficiency of variable-order models for compression.

    Conclusions:

    • The developed MEQ method provides a theoretical and practical framework for efficient signal quantization.
    • This approach facilitates improved data compression for image and sound signals.
    • The findings contribute to advancements in information theory and its applications in multimedia signal processing.