发表机构
University of Utah; Oregon State University; Ohio State University（犹他大学; 俄勒冈州立大学; 俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FeatureZ是针对结构化体标量场的有损压缩通用框架，通过保留逐点界与一致星形分类实现多样特征保留，开销极小，压缩比优于或相当于专用方法。
AI 中文摘要
几何与拓扑特征（如等值面、分位数、合并树及Morse-Smale复形）是医学成像、气候科学、材料科学、天文学等多领域科学数据分析与可视化的核心。然而，多数科学数据有损压缩器仅提供逐点误差保证，无法保留衍生特征；现有特征保留型压缩器开发难度大，且通常针对单一特征定制。本文提出FeatureZ，一种针对结构化体标量场的有损压缩框架，可保留广泛类别的几何与拓扑特征。具体而言，FeatureZ将特征保留型压缩建模为：保留逐点上下界，同时对底层结构化网格中每个点的星形（即其关联单元）进行一致分类。尽管并非所有特征保留概念都适配该框架，但现有压缩器针对的诸多特征与拓扑描述符均适用此框架。FeatureZ提供该建模下特征保留型压缩的高效实现，其作为增强层运行，优化现有有损压缩器的输出：先应用量化以强制逐点界，再采用迭代过程确保星形分类一致。实验表明，FeatureZ在压缩过程中可保留多样特征，且开销极小，实现的压缩比与针对单一特征的专用方法相当或更优。
英文摘要
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.
Comments27 pages, 16 figures, to be presented at IEEE Vis 2026 (and published in IEEE TVCG 2027)
