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.

我们提出了一种轻量级的学习视频编解码器，每个解码像素只需要 900 个乘法和 800 个总参数。它结合了过拟合的图像编解码器 Cool-chic，并通过一个间接编码模块来利用视频的时间冗余。该模型能够以低延迟和随机访问配置压缩视频，达到接近 AVC 的速率失真，并超越其他过拟合编解码器如 FFNeRV。该系统已开源：orange-opensource.github.io/Cool-Chic。

冷酷时尚的视频：以 800 个参数进行学习式视频编码

Cool-chic video: Learned video coding with 800 parameters

We propose Skip-Convolutions to leverage the large amount of redundancies in
video streams and save computations. Each video is represented as a series of
changes across frames and network activations, denoted as residuals. We
reformulate standard convolution to be efficiently computed on residual frames:
each layer is coupled with a binary gate deciding whether a residual is
important to the model prediction,~\eg foreground regions, or it can be safely
skipped, e.g. background regions. These gates can either be implemented as an
efficient network trained jointly with convolution kernels, or can simply skip
the residuals based on their magnitude. Gating functions can also incorporate
block-wise sparsity structures, as required for efficient implementation on
hardware platforms. By replacing all convolutions with Skip-Convolutions in two
state-of-the-art architectures, namely EfficientDet and HRNet, we reduce their
computational cost consistently by a factor of 3~4x for two different tasks,
without any accuracy drop. Extensive comparisons with existing model
compression, as well as image and video efficiency methods demonstrate that
Skip-Convolutions set a new state-of-the-art by effectively exploiting the
temporal redundancies in videos.

通过 Skip-Convolutions 算法，有效利用视频流的时间冗余，将所有卷积替换成 Skip-Convolutions，可以将计算成本降低 3 至 4 倍，同时不会降低准确性，从而显著提高模型压缩和图像效率。