Image Synthesis And Coding Using Gaussian Markov Random Field Models

R. Chellappa; R. Bagdazian

doi:10.1117/12.965982

17 March 1983 Image Synthesis And Coding Using Gaussian Markov Random Field Models

R. Chellappa, R. Bagdazian

Proceedings Volume 0359, Applications of Digital Image Processing IV; (1983) https://doi.org/10.1117/12.965982
Event: 26th Annual Technical Symposium, 1982, San Diego, United States

Abstract

The purpose of this paper is to illustrate the usefulness of two- dimensional (2-D) Gaussian Markov random field models for synthesis and coding of textures. The MRF models used are non causal; the mean of observation y(s) at position s is written as a linear weighted sum of observations surrounding s in all directions. The method of least squares is used to obtain estimates of the model parameters. The model is then used with appropriate boundary conditions to regenerate the original image. Results obtained indicate that this method could be used to code textures at low bit rates.

Citation Download Citation

R. Chellappa and R. Bagdazian "Image Synthesis And Coding Using Gaussian Markov Random Field Models", Proc. SPIE 0359, Applications of Digital Image Processing IV, (17 March 1983); https://doi.org/10.1117/12.965982

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available