No-reference image quality assessment based on high order derivatives

Abstract

Research in human visual perception has found that the sense of natural scences cannot be conveyed only through lines and edges. It also needs the knowledge of texture regions within the image, which can be obtained through the analysis of higher derivatives. Inspired by the research from neuroscience that high order derivatives can capture the details of image structure, we propose a novel simple yet effective blind image quality assessment (IQA) metric based on high order derivatives (BHOD). In the proposed metric, we extract multi-scale structural features up to fourth order image derivatives, to obtain the image structural features. Support vector regression (SVR) is used to learn the mapping between feature space and subjective opinion scores. The proposed method is extensively evaluated on three image databases and shows highly competitive performance to state-of-the-art NR-IQA methods.