Neural Weight Step Video Compression
- URL: http://arxiv.org/abs/2112.01504v1
- Date: Thu, 2 Dec 2021 18:53:05 GMT
- Title: Neural Weight Step Video Compression
- Authors: Mikolaj Czerkawski, Javier Cardona, Robert Atkinson, Craig Michie,
Ivan Andonovic, Carmine Clemente, Christos Tachtatzis
- Abstract summary: In this work, we suggest a set of experiments for testing the feasibility of compressing video using two architectural paradigms.
We propose a novel technique of encoding frames of a video 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.
- Score: 0.5772546394254112
- License: http://creativecommons.org/licenses/by/4.0/
- 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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