Recently,　many　compression　algorithms　are　applied　to　decrease　the　cost　of　video　storage　and　transmission.　This　will　introduce　undesirable　artifacts,　which　severely　degrade　visual　quality.　Therefore,　Video　Compression　Artifacts　Removal　(VCAR)　aims　at　reconstructing　a　high-quality　video　from　its　corrupted　version　of　compression.　Generally,　this　task　is　considered　as　a　vision-related　instead　of　media-related　problem.　In　vision-related　research,　the　visual　quality　has　been　significantly　improved　while　the　computational　complexity　and　bitrate　issues　are　less　considered.　In　this　work,　we　review　the　performance　constraints　of　video　coding　and　transfer　to　evaluate　the　VCAR　outputs.　Based　on　the　analyses,　we　propose　a　Spatial-Temporal　Attention-Guided　Enhancement　Network　(STAGE-Net).　First,　we　employ　dynamic　filter　processing,　instead　of　conventional　optical　flow　method,　to　reduce　the　computational　cost　of　VCAR.　Second,　we　introduce　self-attention　mechanism　to　design　Sequential　Residual　Attention　Blocks　(SRABs)　to　improve　visual　quality　of　enhanced　video　frames　with　bitrate　constraints.　Both　quantitative　and　qualitative　experimental　results　have　demonstrated　the　superiority　of　our　proposed　method,　which　achieves　high　visual　qualities　and　low　computational　costs.