Abstract
This article discusses a neural network-based compression algorithm using error-correcting codes. The use of this algorithm has a number of advantages, on the one hand, the noise-resistant code allows to get rid of potentially high overheads provoked by the use of a neural network, lowering the required value of model accuracy to the value determined by the correctability of the used code. On the other hand, a trained neural network allows data compression without prior transformations.
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Acknowledgments
The study was financially supported by the Russian Science Foundation within of scientific project No. 22-49-02023 “Development and study of methods for obtaining the reliability of tethered high-altitude unmanned telecommunication platforms of a new generation".
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Berezkin, A., Kukunin, D., Kirichek, R. (2022). Research and Development of Data Compression Methods Based on Neural Networks. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds) Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2022. Lecture Notes in Computer Science, vol 13766 . Springer, Cham. https://doi.org/10.1007/978-3-031-23207-7_9
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