Image probability distribution based on generalized gamma function

In this letter, we propose results of distribution tests that indicate that for many natural images, the statistics of the discrete cosine transform (DCT) coefficients are best approximated by a generalized gamma function (G Gamma F), which includes the conventional Gaussian, Laplacian, and gamma probability density functions. The major parameter of the G Gamma F is estimated according to the maximum likelihood (ML) principle. Experimental results on a number Of chi(2) tests indicate that the a G Gamma F can be used effectively for modeling the DCT coefficients compared to the conventional Laplacian and generalized Gaussian function (GGF).