Estimation based on entropy matching for generalized Gaussian PDF modeling

A novel method for estimating the shape factor of a generalized Gaussian probability density function (PDF) is presented and assessed. It relies on matching the entropy of the modeled distribution with that of the empirical data. The entropic approach is suitable for real-time applications and yields results that are accurate also for low values of the shape factor and small data sample. Modeling of wavelet coefficients for entropy coding is addressed and experimental results on true image data reported and discussed.