Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications

Abstract

This report describes a genetic algorithm that evolves optimized sets of coefficients for signal reconstruction under lossy conditions due to quantization. The primary goal of the research described in this final report was to establish a methodology for using genetic algorithms to evolve coefficient sets describing inverse transforms and matched forward/inverse transform pairs that consistently outperform wavelets for image compression and reconstruction applications under conditions subject to quantization error.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2005
Accession Number
ADA437529

Entities

People

  • Brendan Babb
  • Christopher J Wedge
  • Earl Lamson Iii
  • Frank Moore
  • Heather Koyuk
  • Steven Becke

Organizations

  • University of Alaska Anchorage

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Computers
  • Genetic Algorithms
  • Graphical User Interface
  • Image Compression
  • Image Processing
  • Information Processing
  • Information Systems
  • Machine Learning
  • Mathematics
  • Signal Processing
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Organizational Process Management (OPM).

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology