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.
@article{arxiv.2112.01504,
title = {Neural Weight Step Video Compression},
author = {Mikolaj Czerkawski and Javier Cardona and Robert Atkinson and Craig Michie and Ivan Andonovic and Carmine Clemente and Christos Tachtatzis},
journal= {arXiv preprint arXiv:2112.01504},
year = {2023}
}
Comments
Accepted to the pre-registration workshop at NeurIPS 2021