The task of prediction practical efficiency of filtering on the basis of the discrete cosine transform (DCT) methods is considered. It is shown that it is possible to estimate the MSE values of images to be processed by means of calculation rather simple statistics of DCT coefficients. Moreover, the quasi-optimal value of threshold parameter for DCT filtering methods can be easy evaluated as well. The results are presented for different additive Gaussian noise levels and a set of gray-scale test images.Developed method provides an opportunity to decide is it worth applying filtering or not.
Shape parameter estimation procedures for generalized Gaussian distribution are considered. It is shown that the existing estimators can be divided into four groups: maximum likelihood algorithm; moment-based methods; entropy matching estimators and global convergence algorithm. Besides, properties of two recently introduced estimators of shape parameter are discussed. They are based on the combination of two procedures that use the evaluation of the fourth central moment and robust measure of kurtosis. Statistical properties of all considered estimators are investigated by means of defining their bias and variance values for samples of sizes 1000 and 4000 elements and shape parameter values ranging from 0.3 to 2.
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