HNQA: histogram-based descriptors for fast night-time image quality assessment

Abstract

Taking high quality images at night is a challenging issue for many applications. Therefore, assessing the quality of night-time images (NTIs) is a significant area of research. Since there is no reference image for such images, night-time image quality assessment (NTQA) must be performed blindly. Although the field of blind quality assessment of natural images has gained significant popularity over the past decade, the quality assessment of NTIs usually confront complex distortions such as contrast loss, chroma noise, color desaturation, and detail blur, that have been less investigated. In this paper, a blind night-time image quality evaluation model is proposed by generating innovative quality-aware local feature maps, including detail exposedness, color saturation, sharpness, contrast, and naturalness. In the next step, these maps are compressed and converted into global feature representations using histograms. These feature histograms are used to learn a Gaussian process regression (GPR) quality prediction model. The suggested BIQA approach for night images undergoes a comprehensive evaluation on a standard night image database. The results of the experiments demonstrate the superior prediction performance of the proposed BIQA method for night images compared to other advanced BIQA methods despite its simplicity of implementation and execution speed. Hence, it is readily applicable in real-time scenarios.