Image quality assessment measure based on natural image statistics in the Tetrolet domain

TL;DR

This paper introduces a new reduced reference image quality assessment method using Tetrolet transform and Gaussian Scale Mixture modeling to effectively capture local geometric structures and statistical dependencies, evaluated on standard datasets.

Contribution

It proposes a novel image quality measure based on Tetrolet transform and GSM modeling, enhancing the accuracy of quality assessment by capturing local structures and dependencies.

Findings

Outperforms existing measures on Cornell VCL A-57 dataset

Effective in capturing local geometric structures

Utilizes Kullback-Leibler Divergence for degradation quantification

Abstract

This paper deals with a reduced reference (RR) image quality measure based on natural image statistics modeling. For this purpose, Tetrolet transform is used since it provides a convenient way to capture local geometric structures. This transform is applied to both reference and distorted images. Then, Gaussian Scale Mixture (GSM) is proposed to model subbands in order to take account statistical dependencies between tetrolet coefficients. In order to quantify the visual degradation, a measure based on Kullback Leibler Divergence (KLD) is provided. The proposed measure was tested on the Cornell VCL A-57 dataset and compared with other measures according to FR-TV1 VQEG framework.