Restoring high-quality videos from low-quality compressed ones is a crucial research topic in video coding. Most existing methods do not exploit the information in the long-distance compressed frames. Even when they do, these methods ignore the effect of interference information during reconstruction. In this paper, we propose a unique Long-distance Information Filtering (LIF) scheme with the 3D-CNN, which enhances compressed videos by mining filtered and valid information from long-distance frames. Specifically, we propose a practical block, Long-distance Feature Extraction (LFE) block, to model the spatio-temporal relationship within a long temporal range. Furthermore, a progressive Information Filtering (IF) module is proposed in LFE to promote artifact removal and texture restoration by capturing a large effective receptive field, which can significantly boost the effect of LIF. Extensive experiments demonstrate that our method achieves state-of-the-art performance with nearly 25% of the parameters and half of the training volume, which indicates that our model is more lightweight and more efficient.