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This paper proposes a learning-based video codec, specifically used forChallenge on Learned Image Compression (CLIC, CVPRWorkshop) 2020 P-framecoding. More specifically, we designed a compressor network with Refine-Net forcoding residual signals and motion vectors. Also, for motion estimation, weintroduced a hierarchical, attention-based ME-Net. To verify our design, weconducted an extensive ablation study on our modules and different inputformats. Our video codec demonstrates its performance by using the perfectreference frame at the decoder side specified by the CLIC P-frame Challenge.The experimental result shows that our proposed codec is very competitive withthe Challenge top performers in terms of quality metrics.

Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding