RVSIM: a feature similarity method for full-reference image quality assessment

Abstract

Abstract Image quality assessment is an important topic in the field of digital image processing. In this study, a full-reference image quality assessment method called Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM) is proposed. More precisely, a Log-Gabor filter is first used to decompose reference and distorted images, and Riesz transform is performed on the decomposed images on the basis of monogenic signal theory. Then, the monogenic signal similarity matrix is obtained by calculating the similarity of the local amplitude/phase/direction characteristics of monogenic signal. Next, we weight the summation of these characteristics with visual contrast sensitivity. Since the first-order Riesz transform cannot clearly express the corners and intersection points in the image, we calculate the gradient magnitude similarity between the reference and distorted images as a feature, which is combined with monogenic signal similarity to obtain a local quality map. Finally, we conduct the monogenic phase congruency using the Riesz transform feature matrix from the reference image and utilize it as a weighted function to derive the similarity index. Extensive experiments on five benchmark IQA databases, namely, LIVE, CSIQ, TID2008, TID2013, and Waterloo Exploration, indicate that RVSIM is a robust IQA method.

Keywords

• Image quality assessment
• Riesz transform
• Log-Gabor filter
• Gradient magnitude
• Visual contrast sensitivity