An effective general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order.

Blind/no-reference image quality assessment (NR-IQA) aims to assess the quality of an image without any reference image. In this paper, we propose an effective and efficient general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order, based on the observation that the variation of the marginal distribution of the relative order coefficients effectively reflect the degree of warping caused by different types of image distortions. In the literature, gradient-relevant methods have had a big success in full-reference (FR) IQA and reduced-reference (RR) IQA. Inspired by these, we extend it to NR-IQA in this paper. Notice that the NSS-based models usually extract their features derived from the spatial, wavelet, DCT and spectral domain etc. Unlike these metrics, the proposed method firstly extracts 32 natural scene statistics features of the luminance relative order, obtained from the log histograms of log horizontal, vertical, main-diagonal and secondary-diagonal derivatives, along with kurtosis, variance, differential entropy and entropy at two scales. Then a mapping is learned to predict the quality score using a support vector regression. The experimental results on several benchmark databases showed that the proposed method is comparable with the state-of-the-art methods and has a relatively low complexity.