Cool-chic video: Learned video coding with 800 parameters
This addresses video compression for applications needing low-complexity encoding, though it is incremental as it builds on an existing image codec.
The paper tackles video compression by proposing a lightweight learned video codec with 800 parameters and 900 multiplications per decoded pixel, achieving rate-distortion close to AVC while outperforming other overfitted codecs like FFNeRV.
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.