Progressive Bidirectional Feature Extraction and Enhancement Network for Quality Evaluation of Night-Time Images

Abstract

Blind image quality assessment (BIQA) has received increasing attention in the past decades. However, it still remains inadequately researched on BIQA for night-time images suffering from the diverse authentic degradations. Since the intrinsic content degradations of night-time images are highly related to the illumination, how to use the connection between content and illumination to enhance the feature representation ability is the key issue in designing BIQA methods for night-time images. In this paper, we first construct an ultra-high-definition night-time image dataset (UHD-NID) with high image resolution and abundant parameter settings. UHD-NID contains 1600 images with a high resolution of 5616×3744, and each group of images contains ten exposure levels. Then, we conduct subjective assessment and analyze the subjective data to obtain a mean opinion score to each image in UHD-NID. To enhance the feature representation ability in content and illumination, we propose a progressive bidirectional feature extraction and enhancement network (PBFEE-Net). In addition, we use a decomposition network to decompose the input image into the reflectance and illumination, which can facilitate the ability of feature extraction to some extent. The experimental results show that our proposed method achieves superior performance in evaluating the quality of night-time images. The dataset and code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NBUsjl/UHD-NID</uri> .