  We propose a novel neural representation for videos (NeRV) which encodes
videos in neural networks. Unlike conventional representations that treat
videos as frame sequences, we represent videos as neural networks taking frame
index as input. Given a frame index, NeRV outputs the corresponding RGB image.
Video encoding in NeRV is simply fitting a neural network to video frames and
decoding process is a simple feedforward operation. As an image-wise implicit
representation, NeRV output the whole image and shows great efficiency compared
to pixel-wise implicit representation, improving the encoding speed by 25x to
70x, the decoding speed by 38x to 132x, while achieving better video quality.
With such a representation, we can treat videos as neural networks, simplifying
several video-related tasks. For example, conventional video compression
methods are restricted by a long and complex pipeline, specifically designed
for the task. In contrast, with NeRV, we can use any neural network compression
method as a proxy for video compression, and achieve comparable performance to
traditional frame-based video compression approaches (H.264, HEVC \etc).
Besides compression, we demonstrate the generalization of NeRV for video
denoising. The source code and pre-trained model can be found at
https://github.com/haochen-rye/NeRV.git.
