Summary of Hiha: Introducing Hierarchical Harmonic Decomposition to Implicit Neural Compression For Atmospheric Data, by Zhewen Xu et al.
HiHa: Introducing Hierarchical Harmonic Decomposition to Implicit Neural Compression for Atmospheric Data
by Zhewen Xu, Baoxiang Pan, Hongliang Li, Xiaohui Wei
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid development of large climate models necessitates efficient storage and transfer of massive atmospheric data worldwide. Unfortunately, a high-accuracy and high-compressibility compression scheme remains elusive. Implicit Neural Representation (INR) has shown promise in compressing natural data, but its application to atmospheric data is hindered by the complex spatio-temporal properties and variability. To address this issue, we propose Hierarchical Harmonic decomposition implicit neural compression (HiHa), which segments data into multi-frequency signals through harmonic decomposition, then employs a frequency-based hierarchical compression module for each signal. Additionally, HiHa incorporates a temporal residual compression module to accelerate compression by leveraging temporal continuity. Experimental results demonstrate that HiHa outperforms mainstream compressors and other INR-based methods in terms of both compression fidelity and capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to store and send huge amounts of weather data around the world. It’s a big problem! Currently, there isn’t a good way to shrink this data without losing important details. A new technique called Implicit Neural Representation (INR) has shown promise in compressing different types of natural data. However, it struggles when dealing with complex weather patterns. To solve this issue, researchers developed a new method called Hierarchical Harmonic decomposition implicit neural compression (HiHa). HiHa breaks down the data into smaller parts and then uses a special technique to shrink each part separately. It also has a feature that takes advantage of how weather patterns change over time. The results show that HiHa does an excellent job of compressing weather data without losing important details. |
