论文标题
学到的视频编解码器具有富集的重建，用于CLIC P框架编码
Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding
论文作者
论文摘要
本文提出了一种基于学习的视频编解码器，该视频编解码器专门用于挑战学习图像压缩（CLIC，CVPRWORKSHOP）2020 P框架编码。更具体地说，我们设计了一个带有精炼网络的压缩机网络，用于编码残留信号和运动向量。另外，为了进行运动估算，我们引入了一个基于注意力的ME-NET。为了验证我们的设计，我们对模块和不同输入格式进行了广泛的消融研究。我们的视频编解码器通过在Clic P-Frame挑战指定的解码器方面使用完美的参考框来展示其性能。实验结果表明，在质量指标方面，我们提出的编解码器在挑战方面具有竞争力。
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.
