Video　compression　technology　is　significant　to　video　transmission　and　storage.　However,　compression　artifacts　arise　in　videos.　Specifically,　coarse　quantization　eliminates　video　details　and　degrades　visual　quality.　Most　of　artifact　reduction　methods　use　filter　processing　or　Mean　Square　Error　(MSE)loss　that　leads　to　over-smoothing　results.　Moreover,　most　of　the　methods　target　to　reduce　single　image　compression　artifact　instead　of　video　artifact.　In　this　paper,　we　present　an　adversarial　learning　method　with　recurrent　framework　called　Video　Artifact　Reduction　Generative　Adversarial　Network　(VRGAN).　Our　network　contains　a　generator　with　recurrent　framework　that　improves　video　consistency,　a　dense　block　that　enhances　receptive　field　for　large　transform　unit,　and　a　relativistic　discriminator　that　evaluates　the　relationship　between　the　generated　frames　and　the　original　high-quality　frames.　Our　VRGAN　is　able　to　generate　more　realistic　videos.　The　effectiveness　in　reducing　video　compression　artifacts　of　the　method　has　been　demonstrated　qualitatively　and　quantitatively.　The　performance　comparison　with　previous　works　shows　the　superiority　of　the　proposed　method.　©　2019　IEEE.