一种面向误差可控非结构化科学数据压缩的通用框架
A General Framework for Error-controlled Unstructured Scientific Data Compression
AI总结：
针对传统有损压缩方法无法利用非结构化网格空间相干性导致压缩比低的问题，提出多分量误差有界通用压缩框架，通过插值转网格再分别压缩插值结果与残差，在多数据集上实现压缩比2.3-3.5倍的平均提升。
摘要：
数据压缩在降低存储与I/O成本方面发挥着关键作用。传统有损压缩方法主要针对直线网格上的数据，无法利用非结构化网格数据中的空间相干性，导致压缩比欠佳。我们提出了一种多分量、误差有界的压缩框架，旨在提升科学应用中常见的浮点型非结构化网格数据的压缩效果。我们的方法是将网格数据插值到直线网格上，随后分别压缩网格插值结果与插值残差。该方法具有通用性，不受网格类型与拓扑结构限制，可与现有有损压缩器无缝集成以提升性能。我们在两个合成数据集与两个真实仿真数据集的12个变量上评估了该框架。结果表明，该多分量框架在非结构化数据上的表现始终优于现有最先进的有损压缩器，在1e-6到1e-2的误差界范围内，压缩比平均提升2.3-3.5倍。我们进一步探究了网格间距、误差分配等超参数的影响，以在不同数据集中实现最优压缩比。
英文摘要：
Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework designed to enhance the compression of floating-point unstructured mesh data, which is common in scientific applications. Our approach involves interpolating mesh data onto a rectilinear grid and then separately compressing the grid interpolation and the interpolation residuals. This method is general, independent of mesh types and typologies, and can be seamlessly integrated with existing lossy compressors for improved performance. We evaluated our framework across twelve variables from two synthetic datasets and two real-world simulation datasets. The results indicate that the multi-component framework consistently outperforms state-of-the-art lossy compressors on unstructured data, achieving, on average, a $2.3-3.5\times$ improvement in compression ratios, with error bounds ranging from $\num{1e-6}$ to $\num{1e-2}$. We further investigate the impact of hyperparameters, such as grid spacing and error allocation, to deliver optimal compression ratios in diverse datasets.
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