Video coding has effectively addressed the too large data volume of raw videos, however, the achieved compression efficiency comes at the cost of video quality degradation.To improve the visual quality of compressed video, a Detail Recovery Convolutional Neural Network (DRCNN)-based video quality enhancement method is proposed, which consists of a main denoising branch and a detail compensation branch.To effectively extract and eliminate the compression distortions, a Multi-scale Distortion Feature Extraction Block (MDFEB) is added to the main denoising branch, which can pay attention to the distorted areas in the compressed video, and improve the distortion feature learning ability of the proposed DRCNN.Furthermore, to enrich the details in the compressed video, the detail compensation branch adopts a content feature extractor composed of a pre-trained ResNet-50 to provide abundant content features, such as salient objects, shapes, and details, and then involves a Detail Response Block (DRB) to efficiently extract the detailed features from the content features.Extensive experimental results show that the proposed DRCNN achieves the best performance in enhancing the compressed video quality as compared with four representative methods.