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arXiv · 2112.01504

Neural Weight Step Video Compression

Mikolaj Czerkawski·Javier Cardona·Robert Atkinson·Craig Michie·Ivan Andonovic·Carmine Clemente·Christos Tachtatzis
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Abstract

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.

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Mikolaj Czerkawski, Javier Cardona, Robert Atkinson, Craig Michie, Ivan Andonovic, Carmine Clemente, Christos Tachtatzis. 2021-12-02. Neural Weight Step Video Compression. https://arxiv.org/abs/2112.01504

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