The univariate distribution of DCT coefficients of natural images is investigated. The probability density function (PDF) of the coefficients is modelled with the generalised Gaussian function (GGF) which includes the Gaussian and the Laplacian PDF as special cases. The shape parameter of the GGF is estimated according to the maximum likelihood principle, χ2 tests of fit showed that GGFs model the distribution of DCT coefficients more accurately than Laplacian PDFs.
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Frank Müller (1993) studied this question.
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