Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information

Abstract

This paper proposes an image quality evaluation (IQE) metric by considering gradient, visual saliency, and color information. Visual saliency and gradient information are two types of effective features for quality evaluation research. Different regions within an image are not uniformly important for IQE. Visual saliency can find the most attractive regions to the human visual system in a given image. These attractive image regions are more strongly correlated with image quality results. In addition, the degradation of gradient information is related to the structure distortion which is a very important factor for image quality. However, the two types of features cannot accurately evaluate the color distortion of images. In order to evaluate chromatic distortion, this paper proposes the color similarity which is measured in the YIQ color space. The computation of the proposed method begins with the similarity calculation of local gradient information, visual saliency, and color information. Then, the final quality score is obtained by the standard deviation on each similarity component. The experimental results on five benchmark databases (i.e., CSIQ, IVC, LIVE, TID2013, and TID2008) show that the proposed IQE method performs better than other methods in the correlation with subjective quality judgment.