Image probability distribution based on generalized gamma function

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Joon-Hyuk Chang, Jong Won Shin, Nam Soo Kim, Sanjit K. Mitra

Published: 2005, Last Modified: 09 Jan 2026IEEE Signal Process. Lett. 2005EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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/spl Gamma/F), which includes the conventional Gaussian, Laplacian, and gamma probability density functions. The major parameter of the G/spl Gamma/F is estimated according to the maximum likelihood (ML) principle. Experimental results on a number of /spl chi//sup 2/ tests indicate that the G/spl Gamma/F can be used effectively for modeling the DCT coefficients compared to the conventional Laplacian and generalized Gaussian function (GGF).