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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.
Original language | English |
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Number of pages | 8 |
Publication status | Published - 13 Dec 2021 |
Event | NeurIPS 2021 Pre-Registration Workshop: An Alternative Publication Model for Machine Learning research - Online Duration: 13 Dec 2021 → 13 Dec 2021 https://preregister.science |
Workshop
Workshop | NeurIPS 2021 Pre-Registration Workshop |
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Abbreviated title | PreReg@NeurIPS'21 |
Period | 13/12/21 → 13/12/21 |
Internet address |
Keywords
- image encoding
- machine learning
- neural networks
- CbMLP
- video compression
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- 1 Finished
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Doctoral Training Partnership 2018-19 University of Strathclyde | Czerkawski, Mikolaj
Tachtatzis, C. (Principal Investigator), Clemente, C. (Co-investigator) & Czerkawski, M. (Research Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/19 → 11/08/23
Project: Research Studentship - Internally Allocated