  We study how to represent a video with implicit neural representations
(INRs). Classical INRs methods generally utilize MLPs to map input coordinates
to output pixels. While some recent works have tried to directly reconstruct
the whole image with CNNs. However, we argue that both the above pixel-wise and
image-wise strategies are not favorable to video data. Instead, we propose a
patch-wise solution, PS-NeRV, which represents videos as a function of patches
and the corresponding patch coordinate. It naturally inherits the advantages of
image-wise methods, and achieves excellent reconstruction performance with fast
decoding speed. The whole method includes conventional modules, like positional
embedding, MLPs and CNNs, while also introduces AdaIN to enhance intermediate
features. These simple yet essential changes could help the network easily fit
high-frequency details. Extensive experiments have demonstrated its
effectiveness in several video-related tasks, such as video compression and
video inpainting.
