ACIQA: A Dataset and Method for Assessing the Imaging Quality of Automotive Cameras

Yijie Huang, Haoyang Ni, Kaiwei Zhang, Ziheng Jia, Fangfang Lu, Xiongkuo Min, Guangtao Zhai

Abstract: The imaging quality of automotive cameras is crucial in complex driving environments. Therefore, it is essential to conduct subjective experiments that can realistically reflect drivers’ evaluation of the imaging quality of automotive cameras in real traffic scenarios. To accurately assess the imaging quality of automotive cameras, this paper proposes a no-reference quality assessment method with quality scores that are highly consistent with human subjective perception. Initially, this study constructs a new image quality assessment dataset and then obtains the subjective scores of image quality through subjective experiments. The dataset is constructed by using a variety of realistic props to simulate scene elements that might be captured by an automotive camera and are captured using a wide range of cameras with different sensor types, lens focus, and viewing angles, resulting in a dataset of diverse images. The objective quality assessment method proposed in this paper consists of an object detection network and a multi-branch quality evaluation network. The object detection network is responsible for identifying and classifying scene elements, while the multi-branch quality evaluation network performs feature extraction and score regression on various types of elements to effectively evaluate the imaging quality of the automotive cameras. In the experiments, this no-reference quality assessment method is tested on our built dataset, and the results show that the proposed method exhibits the best performance compared with the state-of-the-art image quality assessment methods.