FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression
Authors
Guoxi Liu (The Ohio State University), Yuxiao Li (The Ohio State University), Congrong Ren (The Ohio State University), Robert Underwood (Argonne National Laboratory), Xin Liang (Oregon State University), Bei Wang (University of Utah), Sheng Di (Argonne National Laboratory), Franck Cappello (Argonne National Laboratory), Hanqi Guo (The Ohio State University)
Presentation
- Session
- That's way too big!
- Time
- Thursday, Nov 12, 15:36 – 15:48 (US/Eastern) · session 15:00 – 16:30
- Location
- Hall America center
Links
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Keywords
Scientific data, lossy compression, feature-preserving compression, error control, quantities of interest, visual analytics
Abstract
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.
For Practitioners
Practitioners working with large-scale scientific simulation or imaging data, including climate scientists, cosmologists, materials scientists, lossy-compression users, compressor developers, and visualization practitioners, may be interested in this paper. They can apply its ideas to make more informed compression decisions by comparing compressor configurations through linked visual and quantitative analysis, evaluating how compression affects task-specific quantities of interest such as topological structures or power spectra, and refining compressor choices based on both storage efficiency and downstream scientific validity.