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Pyramidal lattice vector quantization for multiscale image coding.

M Barlaud1, P Sole, T Gaidon

  • 1CNRS, Nice.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
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This study introduces a novel image coding scheme using lattice vector quantization. The method optimizes image compression by balancing distortion and bit rate through lattice truncation and scaling.

Area of Science:

  • Digital image processing
  • Information theory
  • Coding theory

Background:

  • Traditional image compression methods face challenges in balancing distortion and bit rate.
  • Lattice vector quantization offers a promising approach for efficient data representation.

Purpose of the Study:

  • To develop a new image coding scheme utilizing lattice vector quantization.
  • To investigate methods for optimizing the compromise between minimum distortion and bit rate in image compression.

Main Methods:

  • The proposed method employs a two-step process: biorthogonal wavelet transform followed by lattice vector quantization of coefficients.
  • Truncation and scaling of the lattice are crucial for achieving the desired distortion-bit rate trade-off.
  • Analysis involves determining the number of lattice points within a truncated area, particularly for Laplacian sources.

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Main Results:

  • Explicit generating functions for codebook sizes are derived for common lattices such as Z(n), D(n), E(s), and wedge(16).
  • The study provides a mathematical framework for understanding lattice point distribution in quantization.

Conclusions:

  • The developed image coding scheme offers an effective approach to image compression.
  • The findings contribute to the theoretical understanding and practical application of lattice vector quantization in image processing.