No-reference image quality assessment through the von Mises distribution
This is an incremental improvement for image processing applications, offering a new metric for assessing quality without reference images.
The paper tackled the problem of no-reference image quality assessment by introducing a method based on the von Mises distribution of image entropy, showing that higher concentration parameters correlate with best-in-focus noise-free images and that a fitness parameter works for no-contextual images.
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 Rényi 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.