  We propose a method to compress full-resolution video sequences with implicit
neural representations. Each frame is represented as a neural network that maps
coordinate positions to pixel values. We use a separate implicit network to
modulate the coordinate inputs, which enables efficient motion compensation
between frames. Together with a small residual network, this allows us to
efficiently compress P-frames relative to the previous frame. We further lower
the bitrate by storing the network weights with learned integer quantization.
Our method, which we call implicit pixel flow (IPF), offers several
simplifications over established neural video codecs: it does not require the
receiver to have access to a pretrained neural network, does not use expensive
interpolation-based warping operations, and does not require a separate
training dataset. We demonstrate the feasibility of neural implicit compression
on image and video data.
