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Design of trellis coded vector quantizers using Kohonen maps.

Chi Sing Leung1, Lai Wan Chan

  • 1Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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This study introduces trellis coded Kohonen maps (TCKMs), a novel approach simplifying trellis coded vector quantizer (TCVQ) design. TCKMs achieve comparable performance to conventional TCVQs with reduced computational complexity.

Area of Science:

  • Signal Processing
  • Machine Learning
  • Data Compression

Background:

  • Trellis coded vector quantizers (TCVQs) offer superior performance over memoryless vector quantizers.
  • Designing the trellis structure in TCVQs involves significant computational overhead.

Purpose of the Study:

  • To simplify the design process of TCVQs.
  • To introduce a new implementation named trellis coded Kohonen maps (TCKMs).

Main Methods:

  • Application of Kohonen maps to leverage their ordering property.
  • Integration of Kohonen maps within the TCVQ framework to create TCKMs.

Main Results:

  • The proposed TCKMs demonstrate a simplified design process.
  • Simulation results indicate that TCKMs achieve performance comparable to conventional TCVQs.

Related Experiment Videos

Conclusions:

  • Kohonen maps can effectively simplify TCVQ design.
  • TCKMs offer a computationally efficient alternative to traditional TCVQs without sacrificing performance.