Summary of Enhancing Lossy Compression Through Cross-field Information For Scientific Applications, by Youyuan Liu et al.
Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications
by Youyuan Liu, Wenqi Jia, Taolue Yang, Miao Yin, Sian Jin
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper introduces a novel approach to lossy compression for scientific data containing multiple fields. Previous methods have relied on local information from a single target field, but this limitation is overcome by identifying significant cross-field correlations within datasets. The proposed hybrid prediction model uses convolutional neural networks (CNN) to extract cross-field information and combine it with existing local field knowledge, resulting in improved compression ratios without sacrificing data quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers develop a novel lossy compressor that utilizes CNN to extract cross-field information from scientific datasets. This approach leads to better compression ratios and preserves more data details compared to baseline methods. |
Keywords
» Artificial intelligence » Cnn
