We propose a lightweight learned video codec with 900 multiplications per decoded pixel and 800 parameters overall. To the best of our knowledge, this is one of the neural video codecs with the lowest decoding complexity. It is built upon the overfitted image codec Cool-chic and supplements it with an inter coding module to leverage the video's temporal redundancies. The proposed model is able to compress videos using both low-delay and random access configurations and achieves rate-distortion close to AVC while out-performing other overfitted codecs such as FFNeRV. The system is made open-source: orange-opensource.github.io/Cool-Chic.

翻译：我们提出了一种轻量级学习型视频编解码器，其每解码像素仅需900次乘法运算，整体参数量仅为800。据我们所知，这是目前解码复杂度最低的神经视频编解码器之一。该系统基于过拟合图像编解码器Cool-chic构建，通过引入帧间编码模块来利用视频的时间冗余性。所提出的模型能够同时支持低延迟和随机访问配置进行视频压缩，其率失真性能接近AVC标准，且优于FFNeRV等其他过拟合编解码器。该系统已开源：orange-opensource.github.io/Cool-Chic。