Blind Quality Assessment of Night-Time Images Via Weak Illumination Analysis
Miaohui Wang, Yi‐Jing Huang, Jialin Zhang · 2021
Night-time images can be generated by various camera sensors in many practical applications, and hence how to effectively evaluate the quality of night-time images is an essential research topic. In this article, we propose a novel blind quality assessment method for night-time images. By analyzing the characteristic of night-time images, we first investigate the statistical properties of local luminance information based on the brightness level division, and then measure the masking effect on color and structure information caused by weak illumination. Finally, all extracted quality-aware features and the associated subjective ratings are trained via support vector regression to build the quality assessment model. Extensive experiments on a real-world night-image database validate the superiority of the proposed method over several state-of-the-art blind image quality metrics1.