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FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression — arXiv:2608.08386

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.08386
License (as archived): CC0-1.0
Archived: 2026-10-19T08:59:33.236Z
SHA-256 of the archived text: 0695b00272856be9a5278a1098e7fa8f470a8037e1fef953005617e06fe71a15
Stable URL of this page: https://metraipolis.com/documento/dc_96923bb9dfd6

How to cite

FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression — arXiv:2608.08386 — arXiv (en), https://arxiv.org/abs/2608.08386 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_96923bb9dfd6 (Metraipolis archive dc_96923bb9dfd6, sha256 0695b00272856be9a5278a1098e7fa8f470a8037e1fef953005617e06fe71a15)

BibTeX

@misc{dc96923bb9dfd6,
  title = {FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression — arXiv:2608.08386},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.08386},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_96923bb9dfd6; sha256 0695b00272856be9a5278a1098e7fa8f470a8037e1fef953005617e06fe71a15},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.08386. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-11 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Graphics (cs.GR).
Pagina del lavoro: https://arxiv.org/abs/2608.08386 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:54:56.001Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).