Featurez framework compresses scientific data while preserving key features
FeatureZ: A General Framework for Feature-Preserving Compression via Pointwise Bounds and Star Classification
GraphicsInformation Theory
Summary
Many ways to compress scientific data lose important shapes and details needed for analysis. The authors created FeatureZ, a framework that helps keep these important features during compression by carefully preserving certain pointwise bounds and classifications on the data structure. It works as an add-on to existing compressors and keeps important scientific features intact with little extra cost, making data smaller but still useful.
What this means in practice
- •For medical imaging teams: Compress volumetric medical scans while preserving important anatomical structures for diagnosis and visualization tasks.
- •For climate data engineers: Shrink large climate simulation datasets without losing critical topological features needed for downstream analysis and visualization.
Authors
Nathaniel Gorski, Xin Liang, Hanqi Guo, Bei Wang
Abstract
Geometric and topological features, such as isosurfaces, quantiles, merge trees, and Morse-Smale complexes, are central to the analysis and visualization of scientific data across diverse domains, including medical imaging, climate science, materials science, and astronomy. However, most lossy compressors for scientific data provide only pointwise error guarantees and do not preserve derived features. Existing feature-preserving compressors are often difficult to develop and typically tailored to a single feature. In this paper, we introduce FeatureZ, a lossy compression framework for structured volumetric scalar fields that can preserve a wide class of geometric and topological features. In particular, FeatureZ frames feature-preserving compression as preserving pointwise upper and lower bounds together with a consistent classification of each point's star (i.e., its incident cells) in the underlying structured mesh. Although not all notions of feature preservation fit this framework, many features and topological descriptors targeted by existing compressors do. FeatureZ provides an efficient implementation of feature-preserving compression under this formulation. In particular, FeatureZ operates as an augmentation layer that refines the output of an existing lossy compressor. It first applies quantization to enforce pointwise bounds, and then employs an iterative procedure to ensure consistent star classification. We demonstrate that FeatureZ preserves diverse features during compression with minimal overhead, achieving compression ratios comparable to or better than methods specialized for individual features.