Video　compression　artifact　removal　focuses　on　enhancing　the　visual　quality　of　compressed　videos　by　mitigating　visual　distortions.　However,　existing　methods　often　struggle　to　effectively　capture　spatio-temporal　features　and　recover　high-frequency　details,　due　to　their　suboptimal　adaptation　to　the　characteristics　of　compression　artifacts.　To　overcome　these　limitations,　we　propose　a　novel　Spatio-Temporal　and　Frequency　Fusion　(STFF)　framework.　STFF　incorporates　three　key　components:　Feature　Extraction　and　Alignment　(FEA),　which　employs　SRU　for　effective　spatiotemporal　feature　extraction;　Bidirectional　High-Frequency　Enhanced　Propagation　(BHFEP),　which　integrates　HCAB　to　restore　high-frequency　details　through　bidirectional　propagation;　and　Residual　High-Frequency　Refinement　(RHFR),　which　further　enhances　high-frequency　information.　Extensive　experiments　demonstrate　that　STFF　achieves　superior　performance　compared　to　state-of-the-art　methods　in　both　objective　metrics　and　subjective　visual　quality,　effectively　addressing　the　challenges　posed　by　video　compression　artifacts.　Trained　model　available:　https://github.com/Stars-WMX/STFF.