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.

高质量的夜间成像对于视频监控、自动驾驶和消费电子产品至关重要。然而，与白天成像不同的是，夜间成像存在光线不足、照度不均、对焦困难等缺点，使得夜间成像质量备受关注。因此，一种实用的夜间图像质量评价方法对于控制和改进夜间成像系统非常有前景。为此，在本文中，我们提出了一种盲图像质量评估（BIQA）方法来量化夜间图像质量。具体来说，在所提出的方法中，我们通过研究与图像质量高度相关的基本图像属性来测量夜间图像质量，例如亮度、饱和度、锐度、噪声、对比和语义。特定功能旨在正确表征图像属性。然后我们采用支持向量回归 (SVR) 方法通过提取的质量感知特征来推断图像质量。在具有代表性的夜间图像数据库上对所提出的夜间图像 BIQA 方法进行了全面评估。实验结果表明，所提出的夜间图像 BIQA 方法比其他最先进的 BIQA 方法具有更好的预测性能。