Applications of Stochastic Models for Image Data Compression.

Abstract

Intraframe image data compression systems are analyzed in this thesis using stochastic modeling concepts. The formulations of stochastic image models are obtained from different classes of partial differential equations. Their application to the coding problem shows the connection between predictive, hybrid and transform coding schemes. The resulting coding schemes are evaluated in terms of tangible system terms such as signal to noise ratio, mean square error and rate distortion curve. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1979
Accession Number
ADA078781

Entities

People

  • Anil K. Jain
  • Shenq-huey Wang

Organizations

  • University of California, Davis

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Cyber
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computational Science
  • Data Compression
  • Differential Equations
  • Electrical Engineering
  • Equations
  • Image Processing
  • Information Theory
  • Kalman Filters
  • Mathematical Filters
  • Partial Differential Equations
  • Probability Distributions
  • Random Variables
  • Statistical Analysis
  • Statistics
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.