SherVine: A graphical dependency modeling for shearlet transform and its application in image quality assessment.

Many applications of image processing, statistical modeling of frequency domain coefficients is considered by researchers as an approach. In this paper, we present a probabilistic graphical model based on the Vine Copula approach called SherVine(Shearlet Vine) to model the shearlet transform coefficients jointly. The proposed model is an extension of joint modeling with the help of Copula's theory, which can diversely model a pair of shearlet coefficients. Also, the graphical representation of SherVine leads to a better understanding of dependencies types between shearlet coefficients. In the second part of this paper, we propose a novel image quality assessment(IQA) approach based on SherVine called IQA-SherVine. IQA-SherVine uses the parameters of the SherVine model as a feature in the IQA framework. The extracted features are modeled using the decision tree algorithm in IQA-SherVine. The results show that IQA-SherVine performs very well compared to the statistical and deep learning based state-of-the-art methods. [ABSTRACT FROM AUTHOR]