  A variety of compression methods based on encoding images as weights of a
neural network have been recently proposed. Yet, the potential of similar
approaches for video compression remains unexplored. In this work, we suggest a
set of experiments for testing the feasibility of compressing video using two
architectural paradigms, coordinate-based MLP (CbMLP) and convolutional
network. Furthermore, we propose a novel technique of neural weight stepping,
where subsequent frames of a video are encoded as low-entropy parameter
updates. To assess the feasibility of the considered approaches, we will test
the video compression performance on several high-resolution video datasets and
compare against existing conventional and neural compression techniques.
