SURVEILLANCE VIDEO QUALITY ASSESSMENT BASED ON QUALITY RELATED RETRAINING

Surveillance videos have been widely used in many vision-based systems, supporting intelligent tasks such as object detection and tracking. However, the quality of surveillance videos suffers from poor weather conditions and inevitable compression error, which may have a negative influence on the performance of such tasks. Therefore, accurately distinguishing distortions and predicting severity levels are crucial. In this paper, we propose a quality related retraining framework as well as a no-reference (NR) multi-task video quality assessment (VQA) model to tackle the challenge of surveillance videos quality assessment. The quality related retraining framework operates on a synthetic VQA database. The proposed NR VQA method utilizes both spatial and temporal information by using ResNet50 and SlowFast. Then multiple distortion detection heads are applied to predict the severity levels for corresponding distortions. The experimental results show that the proposed method gains competitive performance on the Video Surveillance Quality Assessment Dataset (VSQuAD). The ablation study further confirms the contributions of the quality related retraining framework, spatial information, and temporal information.