FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression

2026-08-08

AI Summary

This work addresses the challenge of lossy compression for scientific simulation data, which requires balancing high compression ratios against the fidelity of quantities of interest (QoIs)—a trade-off that is highly data- and task-dependent and thus difficult to navigate. To tackle this complexity, the paper introduces the first QoI-aware, human-in-the-loop interactive visual analytics framework that integrates multiple lossy compression algorithms, quantitative evaluation metrics, and web-based visualization tools. This framework enables diverse users to efficiently explore and understand the trade-offs between compression strategies and QoI preservation. Through three representative case studies, the authors demonstrate that the framework substantially improves user decision-making accuracy and efficiency in selecting compression methods, interpreting their underlying mechanisms, and assessing feature fidelity.

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.

Problem

• lossy compression
• quantities of interest
• scientific data
• compression fidelity
• visual analytics

Innovation

• visual analytics
• lossy compression
• quantities of interest
• human-in-the-loop
• scientific data