A fast estimation method for the generalized Gaussian mixture distribution on complex images

In this paper, a fast estimation method which is developed for estimating the parameters of the generalized Gaussian distribution (GGD) mixture model is presented. In practice, the frequency data observed from complex image intensity is modeled as a random variable, which could be approximated by a GGD mixture model. To seek the “best-practice” parameter estimates of the model, the new method intends to combine the merits of the estimation efficiency via statistical estimators and the computation efficiency via evolutionary algorithms, termed the EP2 method. The EP2 method is designed particularly for estimating widely ranged shape parameters that characterizes the Gaussian family densities, including sub- and super-Gaussian densities. Experimental results obtained by modeling both simulated data and complex image histogram data arising from non-Gaussian sources are employed to illustrate the estimation effectiveness and efficiency of the proposed method.