Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications
This work addresses the need for more efficient data storage in scientific applications, offering an incremental improvement over existing methods.
The paper tackled the problem of limited compression ratios in lossy compression for scientific data by identifying cross-field correlations and proposing a hybrid prediction model using CNN to integrate cross-field with local information, resulting in up to 25% improvement in compression ratios under specific error bounds while preserving data quality.
Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous approaches use local information from a single target field when predicting target data points, limiting their potential to achieve higher compression ratios. In this paper, we identified significant cross-field correlations within scientific datasets. We propose a novel hybrid prediction model that utilizes CNN to extract cross-field information and combine it with existing local field information. Our solution enhances the prediction accuracy of lossy compressors, leading to improved compression ratios without compromising data quality. We evaluate our solution on three scientific datasets, demonstrating its ability to improve compression ratios by up to 25% under specific error bounds. Additionally, our solution preserves more data details and reduces artifacts compared to baseline approaches.