No-reference image quality assessment with center-surround based natural scene statistics

In this paper, we propose an efficient no-reference image quality assessment (NR-IQA) method dubbed Center-Surround based Blind Image Quality Assessment (CS-BIQA). Our proposed method employs the Difference of Gaussian (DoG) model to decompose images into several frequency bands, considering the center-surround effect and multi-channel attribute of human visual system (HVS). The integrated natural scene statistics (NSS) features can be further derived from all DoG bands. After that, regression models between the integrated features and associated subjective assessment scores are learned on the training dataset. Subsequently, the learned models are used to predict the quality scores of test images. The main contribution of this paper is twofold. Firstly, the empirical distributions of DoG bands of images are proven to be a Gaussian-like distribution. And thus, the NSS features can be employed to represent the perceptual quality of images. Secondly, different types of distortions are observed to affect different frequency components of images. So, the integrated features extracted from multi-frequency bands are employed in CS-BIQA to achieve stronger distinguishable capability of image quality. Excessive experiments are conducted to indicate that our proposed CS-BIQA metric can represent the perceptual characteristics of HVS. The results on popular IQA databases demonstrate that the CS-BIQA metric is competitive with the state-of-the-art relevant IQA metrics. Furthermore, our proposed method has very low computational complexity, making it more suitable for real-time applications.