Making　full　use　of　spatial-temporal　information　is　the　key　factor　for　removing　compressed　video　artifacts.　Recently,　many　deep　learning-based　compression　artifact　reduction　methods　have　emerged.　Among　them,　a　series　of　methods　based　on　deformable　convolution　have　shown　excellent　capabilities　in　spatio-temporal　feature　extraction.　However,　local　deformable　offset　prediction　and　pixel-wise　inter-frame　feature　alignment　in　the　unidirectional　form　limit　the　full　utilization　of　temporal　features　in　the　existing　method.　Additionally,　compressed　video　shows　inconsistent　degrees　of　distortion　on　different　frequency　components,　and　their　restoration　difficulty　is　also　nonuniform.　For　the　above　problems　presented　by　existing　methods,　we　propose　an　enlarged　motion-aware　and　frequency-aware　network　(EMAFA)　to　further　extract　spatio-temporal　information　and　enhance　information　of　different　frequency　components.　To　perceive　different　degrees　of　motion　artifacts　between　compressed　frames　as　accurately　as　possible,　we　design　a　bidirectional　dense　propagation　pattern　with　pixel-wise　and　patch-wise　deformable　convolution　(PIPA)　module　in　the　feature　domain.　In　addition,　we　propose　a　multi-scale　atrous　deformable　alignment　(MSADA)　module　to　enrich　spatio-temporal　features　in　image　domain.　Moreover,　we　design　a　multi-direction　frequency　enhancement　(MDFE)　module　with　multiple　direction　convolution　to　enhance　the　features　of　different　frequency　components.　The　experimental　results　show　that　the　proposed　method　performs　better　than　the　state-of-the-art　methods　in　both　objective　evaluation　and　visual　perception　experience.　Supplementary　experiments　for　Internet　Streamed　Video　with　hybrid-distortion　demonstrate　that　our　method　also　exhibits　considerable　generalizability　for　quality　enhancement.　IEEE