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An innovative way of calculating the von Mises distribution (VMD) of imageentropy is introduced in this paper. The VMD's concentration parameter and somefitness parameter that will be later defined, have been analyzed in theexperimental part for determining their suitability as a image qualityassessment measure in some particular distortions such as Gaussian blur oradditive Gaussian noise. To achieve such measure, the local R\'{e}nyi entropyis calculated in four equally spaced orientations and used to determine theparameters of the von Mises distribution of the image entropy. Consideringcontextual images, experimental results after applying this model show that thebest-in-focus noise-free images are associated with the highest values for thevon Mises distribution concentration parameter and the highest approximation ofimage data to the von Mises distribution model. Our defined von Misses fitnessparameter experimentally appears also as a suitable no-reference image qualityassessment indicator for no-contextual images.

No-reference image quality assessment through the von Mises distribution