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

Computer Vision and Pattern Recognition