Blind quality assessment of night-time image

Abstract

High-quality night-time imaging is crucial to video surveillance, automatic drive and consumer electronics. However, different from day-time imaging, night-time imaging suffers from some disadvantages, such as low light, uneven illumination, difficult focusing, etc., which raises a great concern to the night-time imaging quality. Accordingly, a practical night-time image quality evaluation method is very promising to control and improve the night-time imaging system. Toward this end, in this paper, we propose a blind image quality assessment (BIQA) method to quantify the night-time image quality. Specifically, in the proposed method, we measure the night-time image quality by investigating the fundamental image properties, which are highly relevant to the image quality, such as the brightness, saturation, sharpness, noiseness, contrast and the semantics. Specific features are designed to characterize the image properties properly. Then we employ the support vector regression (SVR) method to infer the image quality with the extracted quality-aware features. The proposed BIQA method for night-time images is thoroughly evaluated on a representative night-time image database. Experimental results demonstrate that the proposed BIQA method for night-time images achieves superior prediction performance to other state-of-the-art BIQA methods.