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Published: 13 Jun 2024

Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data

Volume 1, Issue 1

Abstract

Lossy compression has become essential an important technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale..