AI 中文总结
FZ-VIS是面向关注量的科学有损压缩的交互式人在回路可视分析框架，可助力不同用户群体平衡压缩性能与应用特定关注量需求。
AI 中文摘要
现代科学模拟会生成海量数据，使得有损压缩对于高效存储与传输至关重要。然而，在有损压缩下保留关键关注量（QoIs）本质上依赖于数据和任务，需要领域科学家在压缩率与数据保真度之间权衡复杂的取舍。探索这些取舍通常涉及庞大的设计与评估空间，推动了结合交互式探索与定量分析的人在回路方法。为应对这一挑战，我们提出FZ-VIS，一种用于面向特征的有损压缩设计与可视分析的交互式人在回路框架。FZ-VIS提供基于网页的界面，可快速生成并对比压缩配置，同时配备集成的可视化工具，用于通过视觉检查与定量指标评估重建保真度和关注量（QoI）的保留情况。我们通过涵盖三类代表性用户群体的案例研究证明FZ-VIS的实用性：选择压缩方法的新手用户、检查内部流水线行为的压缩器开发者，以及研究特征保留情况的领域科学家。案例研究表明，FZ-VIS可帮助用户高效探索复杂设计空间，做出明智决策，平衡压缩性能与应用特定的关注量（QoI）需求。
英文摘要
Modern scientific simulations generate massive volumes of data, making lossy compression essential for efficient storage and transmission. However, preserving critical quantities of interest (QoIs) under lossy compression is inherently data- and task-dependent, requiring domain scientists to navigate complex trade-offs between compression ratio and data fidelity. Exploring these trade-offs often involves large design and evaluation spaces, motivating human-in-the-loop approaches that combine interactive exploration with quantitative analysis. To address this challenge, we present FZ-VIS, an interactive framework for human-in-the-loop feature-oriented lossy compression design and visual analytics. FZ-VIS provides a web-based interface for rapidly generating and comparing compression configurations, along with integrated visualization tools for assessing reconstruction fidelity and QoI preservation through both visual inspection and quantitative metrics. We demonstrate the utility of FZ-VIS through case studies involving three representative user groups: novice users selecting compression methods, compressor developers examining internal pipeline behavior, and domain scientists investigating feature preservation. The case studies show how FZ-VIS helps users efficiently navigate complex design spaces and make informed decisions that balance compression performance with application-specific QoI requirements.
Comments12 pages (including appendix). Accepted at IEEE VIS 2026
