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Multiplication free vector quantization using L(1) distortion measure and its variants.

V J Mathews1

  • 1Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
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This study introduces a novel gradient-based method for vector quantization using L(1) distortion. The multiplication-free approach achieves comparable performance to the LBG algorithm in image compression.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Machine Learning

Background:

  • Vector quantization (VQ) is a fundamental technique in data compression.
  • The LBG algorithm is a widely used method for VQ codebook design.
  • The L(1) distortion measure offers an alternative to traditional L(2) distortion.

Purpose of the Study:

  • To propose a novel, computationally efficient gradient-based algorithm for vector quantization codebook design.
  • To demonstrate the effectiveness of the L(1) distortion measure in VQ.
  • To develop a multiplication-free approach for predictive vector quantization of images.

Main Methods:

  • A gradient-based optimization technique is employed for codebook design, avoiding multiplications and median computations.

Related Experiment Videos

  • Convergence of the proposed method is rigorously proven under mild conditions.
  • The algorithm is extended to handle piecewise-linear distortion measures, enabling multiplication-free encoding and design.

    • Main Results:

    • The gradient-based method achieves codebook performance comparable to the LBG algorithm.
    • The proposed technique significantly reduces computational complexity by eliminating multiplications.
    • The method is successfully applied to predictive vector quantization of images, demonstrating its practical viability.

    Conclusions:

    • The gradient-based, multiplication-free VQ approach offers a computationally efficient alternative for image compression.
    • The L(1) distortion measure and its extensions are effective for simplifying VQ algorithms.
    • This research paves the way for efficient, low-complexity image compression techniques.