Objectives The study of image quality evaluation is aimed at the problems of poor image quality evaluation caused by the interference of complex road environment and the absence of real reference images in traffic monitoring scenarios.

Methods We propose a two-stage image quality assessment method for a specific area of the traffic target of interest. First, in order to reduce the influence of image context such as background, an object detection algorithm is proposed to extract the traffic target in the image, and the specific area of the interested traffic target is identified according to the contour, geometric features and license plate recognition algorithm. Second, in order to make up for the lack of real reference images in a specific region, a novel loss function is proposed to generate pseudo-reference images using generative adversarial networks, and to train the generative adversarial networks and image quality evaluators together.

Results The proposed algorithm is evaluated on the dataset of image quality evaluation in traffic monitoring scenario. Compared with other image quality assessment methods, the model of this method is small, and it is at the optimal level in Spearman rank correlation coefficient index and Pearson linear correlation coefficient index.

Conclusions The two-stage image quality assessment method can provide an effective method for the practical application of image quality assessment in complex traffic scenarios.