No-reference image quality assessment through the von Mises distribution

TL;DR

This paper introduces a novel no-reference image quality assessment method based on the von Mises distribution of local Rényi entropy, effectively evaluating distortions like Gaussian blur and noise.

Contribution

It proposes a new image quality metric using von Mises distribution parameters derived from local entropy in multiple orientations, applicable to both contextual and non-contextual images.

Findings

Higher von Mises concentration correlates with better image focus.

The fitness parameter effectively indicates image quality without reference images.

Method performs well on images with Gaussian noise and blur distortions.

Abstract

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