The Guassian distribution model is often used to characterize the statistical behavior of image or other multimedia signal,
and applied in fitting probability density functions of a signal. But, in practically, the probability density function of data
source may be inherently non-Gaussian. As the distribution family covers most of the common distribution types and the
frequency curves provided by the family are as wide as in general use, this paper considers Johnson distribution family to
estimate the unknown parameters and approximate the empirical distribution. The method uses the moments to initialize
the parameters of the distribution family, and then calculates parameters by using EM algorithm. The experiment results
show that the fitted model could depicts quite successfully the both Gaussian and non-Gaussian probability density
function of image intensity, and comparatively the method has low computing complexity.