Image modeling using higher-order statistics with application to predictive image coding

Author

Tekalp, A. ; Ozkan, Mehmet ; Erdem, A.

Author_Institution

Dept. of Electr. Eng., Rochester Univ., NY, USA

fYear

1990

fDate

3-6 Apr 1990

Firstpage

1893

Abstract

The mathematical framework to develop parametric image models based on higher-order statistics (HOS) is discussed. For non-Gaussian images, the model parameters will depend on the HOS if the model has a nonlinear form or if a fidelity criterion other than the mean square error (MSE) is utilized. Three new image models are proposed: (i) a nonlinear model based on the MSE criterion, (ii) a linear model under a criterion other than the MSE, and (iii) a nonlinear model using a criterion other than the MSE. These models are applied to predictive image coding, and the results are compared with those obtained by a linear model based on the MSE criterion

Keywords

encoding; filtering and prediction theory; picture processing; statistical analysis; MSE; higher-order statistics; linear model; mean square error; model parameters; nonGaussian images; nonlinear model; parametric image models; predictive image coding; Higher order statistics; Image coding; Laplace equations; Mathematical model; Mean square error methods; Parametric statistics; Predictive models; Probability; Random processes; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115869

Filename

115869