Optimal approximation model of autocorrelation function of digital image

Abstract

Non-stationary stochastic signal was divided into piecewise stationary stochastic signal,and reflecting the sig-nal’s characteristics by autocorrelation function of the piecewise stationary stochastic signal.Generally,the autocorrela-tion function was the base of selecting signal base for signal representation.For expressing non-stationary stochastic sig-nal in a precise and effective way,based on the analysis of the natural characteristics of ARMA model and Markov proc-ess,a kind of multi-parameter estimation model of autocorrelation function for piecewise stationary stochastic process was proposed.The computational complexity was reduced,and the approximation effect was improved.Furthermore,the multi-parameter estimation model could also be adapted to the complex non-stationary stochastic signal,The computer simulation demonstrates that the approximation error was decreased significantly.

Keywords

• stochastic process;piecewise stationary process;nonlinear approximation;auto correlation