With the development of multimedia services and mobile networks, in order to achieve the data interconnection and interoperability of smart cities, medium and high point surveillance equipment, Internet of Things (IoT), AI and other technologies make the data collection, uploading and organization of urban surveillance video automated, effectively providing active discovery, timely warning and early intervention of urban security risks. However, the huge data volume of surveillance video puts huge pressure on the transmission networks, for this reason, this paper proposes a neural network model image and video quality enhancement algorithm based on semantic feature extraction. The algorithm firstly proposes a joint optimization of the degradation model and then reconstruction model to address the problems of existing image and video recovery enhancement methods. The proposed model is validated on a publicly available dataset and compared with existing algorithms, the proposed method can achieve a 50% improvement in score compared to PULSE method and is close to the original HD image and video quality. In terms of user evaluation, 81% of the reconstruction results were found to be superior to the comparison algorithm. The results show that the proposed algorithm has higher reconstructed image and video quality.