A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information

Abstract

A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.

Research on x-ray weld seam image quality evaluation methods

“…Gao M [3] broke through the classical algorithmic framework based on local information and proposed a framework based on nonlocal information, and constructed a nonlocal gradient-based image quality evaluation algorithm within this framework. Xie J [4] proposed an algorithm based on nonlinear Gaussian mean difference. The method of combining multiple edge detection operators is proposed by Shen L [5] to avoid the limitations of a single operator.…”