Data Compression Using Artificial Neural Networks

Abstract

This thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design are adapted to the neural network design to reduce the computational cost and hardware requirements. The results show that the new algorithm provides a substantial reduction in computational costs and an improvement in performance.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA246906

Entities

People

  • Bruce E. Watkins

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • California
  • Compression
  • Computer Programming
  • Computers
  • Data Compression
  • Data Rate
  • Data Sets
  • Delta Modulation
  • Engineering
  • Neural Networks
  • Parallel Computing
  • Parallel Processing
  • Self Organizing Systems
  • Signal Processing
  • Supervised Machine Learning
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Life Cycle Cost Analysis
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks