The Multilinear Compound Gaussian Distribution

Abstract

We introduce a novel generalization of the compound Gaussian (CG) (or Gaussian Scale Mixture) distribution which extends the Gaussian component of the CG model to a multilinear distribution. The resulting model, which we call the Multilinear Compound Gaussian (MCG) distribution, subsumes both GSM and the previously developed MICA distributions as complementary special cases; thereby allowing us to model a richer class of stochastic phenomena. First we derive the structural characterization of the MCG distribution and develop some of its important theoretical properties. Thereafter we describe a parameter estimation algorithm for learning this model from sample data, and then deploy this for modeling textures, including natural (i.e. optical) and SAR images. Our simulation results demonstrate how, for each case, we obtain improved performance over the CG model; thus indicating the versatility of the MCG model in accurately modeling various natural phenomena of interest.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2012
Accession Number
ADA576355

Entities

People

  • Alan C. Bovik
  • Raghu G. Raj

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Science
  • Equations
  • Gaussian Distributions
  • Image Processing
  • Image Reconstruction
  • Images
  • Information Processing
  • Information Science
  • Linear Systems
  • Models
  • Optical Images
  • Probabilistic Models
  • Probability
  • Random Variables
  • Simulations

Readers

  • Astronomy and Astrophysics.
  • Computational Modeling and Simulation
  • Neural Network Machine Learning.