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

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read FZ-VIS claims that quantities-of-interest-aware lossy compression design can be unified into a single interactive visual analytics workflow, and supports the claim with case studies across three user groups.

desk verdict FZ-VIS is a genuine, well-integrated system for QoI-aware compression design, but its 'helps users' claim outruns the narrative-case-study evidence. read the letter →

arxiv 2608.08386 v1 pith:FJZXLHNJ submitted 2026-08-09 cs.HC cs.GR

Guoxi Liu , Yuxiao Li , Congrong Ren , Robert Underwood , Xin Liang , Bei Wang , Sheng Di , Franck Cappello
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Hanqi Guo
This is my paper · ORCID
classification cs.HCcs.GR
keywords lossycompressionquantitiesofinterestvisualanalyticsMorse-Smalesegmentationpowerspectrumhuman-in-the-looperror-boundedinteractiveexploration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that choosing and tuning a lossy compressor for scientific data should be treated as a human-in-the-loop visual analytics problem, and that a single interactive environment can replace the usual sequence of scripts, benchmark tables, and separate analysis tools. It presents FZ-VIS, a web-based framework that couples the compressor design space (module choices, error bounds, execution variants) with a scientific evaluation space (fidelity metrics, spatial comparison, and application-specific quantities of interest). The motivating gap is that error-bounded compressors may preserve pointwise error while destroying downstream features such as topological segmentations or power spectra, and existing evaluation tools do not make those failures visible during compressor selection. If the central claim holds, scientists can judge a compressor by whether the features their analysis depends on survive, and can correct failures in the same workflow.

What carries the argument

The central mechanism is the linked-view workflow that ties the compressor design space to the scientific evaluation space. Concretely: users compose a reference compressor pipeline and branch it into configuration families via parallel coordinates and a force-directed graph; a batch runner executes variants and stores stage-wise intermediates; quantitative views (rate-distortion diagrams, rankings, spectral-error curves) are linked to spatial views (error maps, region-of-interest comparisons); and pluggable QoI modules compute topology and spectrum. The topological module uses Morse-Smale segmentations - partitions of a scalar field into regions sharing the same pair of extrema - to count false structures, and the spectral module compares power spectra $P(k)=\sum_{u^2+v^2+w^2=k^2}|X'_{u,v,w}|^2$. Correction is provided by MSz, an edit-based method that adjusts decompressed values within the error bound to restore Morse-Smale segmentations, and FFCz, which projects reconstruction errors onto the intersection of spatial and frequency-domain constraints.

What would settle it

Conduct the same compressor-selection and QoI-validation tasks in a controlled study with two groups, one using FZ-VIS and one using scripted configuration plus standard metric and QoI scripts, and compare task completion time, number of configurations attempted, and the QoI preservation of the final choice. If the FZ-VIS group is not faster or does not end with comparable-or-better configurations, the framework's central value claim fails.

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Extended reading notes

Core claim

FZ-VIS's central claim is that unifying compressor configuration, batch evaluation, and quantities-of-interest analysis in one workflow lets users make better-informed compression decisions. The framework structures work in three stages - setup, composition and execution, and analysis and refinement - with generated JSON specifications preserving provenance. Its linked views connect quantitative summaries (rate-distortion curves, ranked candidates) to spatial comparisons (original/decompressed/error maps and region-of-interest detail), and its QoI modules compare Morse-Smale segmentations and power spectra between original and reconstructed data, with optional correction stages that repair topology or spectrum within the same environment. The case studies show a novice selecting a compressor under a compression-ratio and DSSIM target, a developer diagnosing why a regression predictor underperforms the Lorenzo predictor by inspecting residual and quantization-index distributions, a domain scientist finding that tightening error bounds alone does not preserve atmospheric-river Morse-Smale segmentations and then applying a correction, and a cosmology scientist trading spectral fidelity against compression ratio and recovering a high-ratio candidate through a frequency-domain correction.

Load-bearing premise

The load-bearing premise is that interactive, linked-view exploration genuinely improves users' compression decisions compared with existing scripted or benchmark-based workflows; the paper supports this with narrative case studies rather than a controlled measurement of decision time or quality.

Editorial extensions

If this is right

  • Users can determine, in one session, whether any candidate compressor preserves the topological structures or power spectra their downstream analysis needs, rather than learning this after committing to a configuration.
  • Compressor developers can trace a performance difference to a specific pipeline stage by comparing residual histograms, quantization-index spreads, and stage-wise runtimes across variants.
  • QoI failures that pointwise error bounds cannot cure are not dead ends: correction modules can repair Morse-Smale segmentations or spectrum within the same workflow, at a visible cost in compression ratio.
  • Novice users can navigate a large parameter space by branching from a reference pipeline and filtering candidates on rate, fidelity, and QoI criteria, turning blind parameter tuning into structured exploration.
  • Because QoI modules are pluggable stages, the same workflow pattern extends to other application-specific quantities beyond topology and spectrum.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A reasonable next step not explored in the paper is to use the accumulated linked metric and QoI results to train a lightweight surrogate that predicts QoI preservation from compressor parameters, letting users skip the interactive loop for routine datasets.
  • The correction-module pattern suggests a broader design principle: any lossy compressor paired with a post hoc feature-preserving corrector can be exposed as a single tunable unit, so the framework's value may grow as more correctors are built for other quantities.
  • The case studies leave open whether the benefit is largest for novices, who gain guided exploration, or for domain scientists, who gain QoI awareness; a controlled experiment could test which group's decisions improve most.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents FZ-VIS, a web-based visual analytics framework for quantities-of-interest (QoI)-aware lossy compression. The system integrates interactive compressor configuration, batch generation of configuration variants, linked quantitative and spatial comparison views, inspection of intermediate compressor outputs, and QoI-specific analysis modules for Morse-Smale segmentation and power-spectrum fidelity, including correction workflows based on the authors' previously published MSz and FFCz algorithms. The paper claims that FZ-VIS unifies the compressor design space and the scientific evaluation space, enabling novice users, compressor developers, and domain scientists to explore compression trade-offs and make informed decisions. The utility is demonstrated through three narrative case studies on CESM, Hurricane Isabel, Atmospheric Rivers, and NYX data.

Significance. If the central claim is validated, FZ-VIS would address a genuine gap: existing tools such as LibPressio, Z-checker, Foresight, and ParaView do not provide an integrated environment for QoI-aware compression exploration. The framework is thoughtfully designed around a three-stage workflow, and the authors explicitly connect requirements to interface features. The paper also provides reproducibility artifacts, including a workflow graph translated to a JSON specification, open-source code, and supplementary materials. The underlying QoI-correction algorithms (MSz, FFCz) are previously published and are not fitted to the results of this paper. However, the paper's strongest claim—that FZ-VIS 'enables users to explore compressor variants, examine trade-offs, and evaluate feature preservation' (Section 8)—is supported only by illustrative case studies, not by controlled empirical evaluation. The case studies show that the intended workflows can be constructed, but they do not measure whether the framework improves decision-making relative to existing scripted workflows. This limits the current evidentiary basis for the human-in-the-loop benefit that motivates the work.

major comments (4)
  1. [Section 6 and Section 8] The central claim that FZ-VIS 'enables users to explore compressor variants, examine trade-offs, and evaluate feature preservation for downstream analysis' is supported only by narrative case studies. The three case studies in Section 6 demonstrate that the system can be used to perform specific workflow sequences, but they do not include task-completion time, decision accuracy, expert-defined ground truth, or any comparison with the scripted LibPressio/Z-checker/Foresight workflows that the paper positions as the status quo. Since the conclusion (Section 8) repeats this claim as a demonstrated result, the manuscript either needs a controlled task-based user study or a substantially tempered statement of what has been established.
  2. [Appendix A, Table 1] The workflow-level comparison in Table 1 is a self-assessment on qualitative rows such as 'Linked quantitative and spatial comparison' and 'Single-environment workflow (no external scripting).' The table does not report measured performance of any tool by any user. As presented, it cannot support the claim that FZ-VIS reduces coordination overhead or improves workflow efficiency over existing tools; it only states the authors' design intent. A quantitative comparison of task completion or at least a reproducible scripted workflow comparison would be needed to make this row meaningful.
  3. [Section 7] The Limitations and Discussion section lists composition flexibility, QoI coverage, and scalability as limitations, but it does not acknowledge the absence of any empirical user evaluation. Given that the introduction explicitly claims FZ-VIS 'demonstrably helps users efficiently navigate complex design spaces,' the lack of measured evidence is a load-bearing omission. The limitations section should either report the absence of a controlled evaluation or point to a supplementary evaluation that provides such evidence.
  4. [Sections 6.3 and 6.4] The favorable QoI-correction results in Sections 6.3 and 6.4 are obtained using MSz and FFCz, which are the authors' own previously published algorithms. This does not make the results incorrect, but it means the case studies partly showcase the authors' own techniques rather than independently established methods. The paper should explicitly state this relationship and discuss how the results would differ if independent correction algorithms were used, or at least clearly frame the case studies as validations of the integrated workflow rather than of the correction algorithms themselves.
minor comments (5)
  1. [Abstract and Section 6] The abstract states that case studies 'demonstrate the utility' of FZ-VIS, while Section 6 says the case studies are 'intended to illustrate integrated workflow support.' These statements should be aligned; 'illustrate' is more accurate given the absence of controlled evaluation.
  2. [Figure 5] The JSON specification example in Figure 5 contains a mixed quotation mark in the metrics array ("psnr", “mse"). This is likely a typesetting artifact, but it should be corrected for consistency.
  3. [Section 6.3] The Overall Compression Ratio (OCR) is defined in the text, but the definition appears only after Figure 13 is referenced. Placing the definition before the figure reference would improve readability.
  4. [Section 6.4] The text states that ZFP keeps the spectral relative error 'below 0.0020%' within the ROI. Given that this is a relative metric, it would be helpful to clarify the baseline against which this percentage is computed.
  5. [References] The paper cites several accepted-but-not-yet-published works (e.g., [26], [39]). For a journal submission, providing DOIs or stable URLs for accepted works would be helpful.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: FZ-VIS's empirical results are direct measurements, and its QoI modules are prior published algorithms used as components.

full rationale

FZ-VIS is a systems and visual-analytics paper; its claimed contribution is workflow integration rather than a derived first-principles prediction. The numerical evidence cited in Sections 6.1 through 6.4, such as the DSSIM of 0.99848, the compression ratio of 4.6, the spectral relative error below 0.0020 percent, and the OCR values in Figure 13, is reported from executed compression runs and is not a set of parameters fitted to the conclusions those numbers support. The QoI correction modules MSz and FFCz are indeed prior work by overlapping authors, but the paper independently sketches their algorithms in Section 3 and uses them as integrated system components; the case-study outcomes depend on the modules' measured behavior, and no load-bearing claim reduces to an appeal to self-citation. The comparison in Table 1 against LibPressio, ParaView, Z-checker, and Foresight is a qualitative self-assessment and is weak evidence for the stronger 'helps users' claim, but that is a methodological or evidentiary limitation, not a circular derivation. No equation is reused as its own prediction, no fitted parameter is renamed as a result, and no uniqueness theorem or ansatz is imported from prior work to force the framework's design. The absence of a controlled user study is a correctness or validity gap, not a circularity, and the manuscript itself acknowledges several limitations in Section 7. Therefore, no circular steps are present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The framework introduces no new physical entities or fitted constants. Its correctness depends on standard background (Morse-Smale computation, FFT) and on published guarantees of the integrated compressors and correction algorithms, several of which are the authors' prior work.

assumptions (4)
  • domain assumption Morse-Smale segmentation is a meaningful QoI for the atmospheric river and other scientific analyses.
    Section 6.3 assumes preserving MS segmentations is necessary for AR skeleton analysis, citing prior domain work [22].
  • domain assumption Power spectrum preservation is a meaningful QoI for cosmological analysis.
    Section 6.4 assumes the power spectrum is key for cosmology, citing van Daalen et al. [48].
  • domain assumption MSz corrects Morse-Smale segmentations within the global error bound as described.
    Section 5.6 integrates MSz [25] as a correction module, relying on its published guarantee.
  • domain assumption FFCz corrects spectral distortion within user-specified error bounds.
    Section 5.6 integrates FFCz [39] as a correction module, relying on its published guarantee.

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Cite this review

Pith. "Pith review of FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression." pith.science (2026). https://pith.science/paper/FJZXLHNJ

@misc{pith2026260808386,
  author       = {Pith},
  title        = {Pith review of: FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FJZXLHNJ}},
  note         = {Machine review of arXiv:2608.08386}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2608.08386 by the authors.

Figure 1
Figure 1. FZ-VIS supports QoI-aware lossy compression through (A) workflow composition, (B) linked quantitative and spatial views, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Example of Morse-Smale segmentation. The red and blue spheres represent local maxima and minima, respectively. MSz [25] preserves PLMSS in error-bounded lossy compres￾sion by iteratively correcting topological distortions in de￾compressed data. It takes as input a 2D or 3D scalar field f and its decompressed version ˆf obtained from an arbitrary error-bounded lossy compressor with a global error bound ξ . The algori… view at source ↗
Figure 3
Figure 3. Design requirements derived from three user groups: [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (11 more)
Figure 5
Figure 5. Figure 5: Workflow graph and generated JSON specification in FZ-VIS. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 4
Figure 4. Figure 4: Workflow-oriented architecture of FZ-VIS. The workflow [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Compressor configuration generation in FZ-VIS. Starting from [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Linked quantitative comparison and spatial analysis in FZ-VIS. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Comparison of SZ3 using different predictors and ZFP on the [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Performance comparison of SZ3 and ZFP on the CESM dataset [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Comparison of the regression-based predictor and Lorenzo [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 12
Figure 12. Figure 12: Impact of lossy compression on MS segmentations of the [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 13
Figure 13. Figure 13: Overall compression ratio vs. error bound when applying MSz [PITH_FULL_IMAGE:figures/full_fig_p008_13.png]
Figure 15
Figure 15. Figure 15: Left: the FFCz correction module allows users to sweep the [PITH_FULL_IMAGE:figures/full_fig_p009_15.png]
Figure 14
Figure 14. Figure 14: Top: power spectra of different compressor instances on [PITH_FULL_IMAGE:figures/full_fig_p009_14.png]

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Reference graph

Works this paper leans on

63 extracted references · 22 canonical work pages

  1. [1]

    Ayachit.The ParaView Guide: A Parallel Visualization Application

    U. Ayachit.The ParaView Guide: A Parallel Visualization Application. Kitware, Inc., Clifton Park, NY , USA, 2015. 2, 12

    work page 2015

  2. [2]

    A. H. Baker, D. M. Hammerling, S. A. Mickelson, H. Xu, M. B. Stolpe, P. Naveau et al. Evaluating lossy data compression on climate simulation data within a large ensemble.Geoscientific Model Development, 9(12):4381–4403, Dec. 2016. doi: 10.5194/gmd-9-4381-2016 2

    doi:10.5194/gmd-9-4381-2016 2016

  3. [3]

    A. H. Baker, A. Pinard, and D. M. Hammerling. On a Structural Similarity Index Approach for Floating-Point Data.IEEE Transactions on Visualization and Computer Graphics, 30(9):6261–6274, 2024. doi: 10. 1109/TVCG.2023.3332843 7

    arXiv 2024

  4. [4]

    Bruckner and T

    S. Bruckner and T. Möller. Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design.IEEE Transactions on Visualization and Computer Graphics, 16(6):1468–1476, 2010. doi: 10. 1109/TVCG.2010.190 3

    work page 2010

  5. [5]

    Cappello, R

    F. Cappello, R. Underwood, Y . Alexeev, A. Baker, E. Bozda˘g, M. Burtscher et al. What to Support When You’re Compressing: The State of Practice Gaps and Opportunities for Scientific Data Compression. InProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1966–1979. Association for Computing Mac...

    doi:10.1145/3712285 1966

  6. [6]

    H. Chen, S. Zhang, W. Chen, H. Mei, J. Zhang, A. Mercer et al. Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration.IEEE Transactions on Visualization and Computer Graphics, 21(9):1072–1086, 2015. doi: 10.1109/TVCG.2015.2410278 3

    arXiv 2015

  7. [7]

    L. P. Deutsch. GZIP file format specification version 4.3. Request for Comments RFC 1952, Internet Engineering Task Force, May 1996. doi: 10.17487/RFC1952 2

    doi:10.17487/rfc1952 1952

  8. [8]

    Di and F

    S. Di and F. Cappello. Fast Error-Bounded Lossy HPC Data Compression with SZ. In2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 730–739, May 2016. doi: 10.1109/IPDPS.2016. 11 2

    doi:10.1109/ipdps.2016 2016

Show all 63 references
  1. [9]

    Di and F

    S. Di and F. Cappello. Optimization of Error-Bounded Lossy Compression for Hard-to-Compress HPC Data.IEEE Transactions on Parallel and Distributed Systems, 29(1):129–143, Jan. 2018. doi: 10.1109/TPDS.2017. 2749300 2

    2018 doi

  2. [10]

    S. Di, J. Liu, K. Zhao, X. Liang, R. Underwood, Z. Zhang et al. A Survey on Error-Bounded Lossy Compression for Scientific Datasets, Apr. 2025. doi: 10.48550/arXiv.2404.02840 2

    2025 doi arXiv

  3. [11]

    E3SM Project

    D. E3SM Project. Energy exascale earth system model v2.1.0. [Computer Software] https://doi.org/10.11578/E3SM/dc.20230110.5, Jan

    doi

  4. [12]

    Fallin, N

    A. Fallin, N. Gorski, T. Agarwal, B. Wang, G. Gopalakrishnan, and M. Burtscher. Fast Topology-Aware Lossy Data Compression with Full Preservation of Critical Points and Local Order.IEEE Transactions on Big Data, pp. 1–14, 2026. doi: 10.1109/TBDATA.2026.3705355 2

    2026

  5. [13]

    Q. Gong, J. Chen, B. Whitney, X. Liang, V . Reshniak, T. Banerjee et al. MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring.SoftwareX, 24:101590, 2023. doi: 10. 1016/j.softx.2023.101590 2

    2023

  6. [14]

    Gorski, X

    N. Gorski, X. Liang, H. Guo, and B. Wang. TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields.IEEE Transactions on Visualization and Computer Graphics, 32(1):527–537, 2026. doi: 10.1109/TVCG.2025.3634844 3

    2026

  7. [15]

    Gorski, X

    N. Gorski, X. Liang, H. Guo, L. Yan, and B. Wang. A General Framework for Augmenting Lossy Compressors With Topological Guarantees.IEEE Transactions on Visualization and Computer Graphics, 31(6):3693–3705,

  8. [16]

    Grosset, C

    P. Grosset, C. M. Biwer, J. Pulido, A. T. Mohan, A. Biswas, J. Patchett et al. Foresight: Analysis That Matters for Data Reduction. InSC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–15, Nov. 2020. doi: 10.1109/SC41405.2020...

    2020 arXiv

  9. [17]

    W. He, J. Wang, H. Guo, K.-C. Wang, H.-W. Shen, M. Raj et al. InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations.IEEE Transactions on Visualization and Computer Graphics, 26(1):23–33, 2020. doi: 10.1109/TVCG.2019.2934312 3

    2020

  10. [18]

    J. D. Hunter. Matplotlib: A 2D Graphics Environment.Computing in Science & Engineering, 9(3):90–95, 2007. doi: 10.1109/MCSE.2007.55 2

    2007 doi

  11. [19]

    J. W. Hurrell, M. M. Holland, P. R. Gent, S. Ghan, J. E. Kay, P. J. Kushner et al. The Community Earth System Model: A Framework for Collaborative Research.Bulletin of the American Meteorological Society, 94(9):1339 – 1360, 2013. doi: 10.1175/BAMS-D-12-00121.1 7

    2013 doi

  12. [20]

    S. Jin, J. Pulido, P. Grosset, J. Tian, D. Tao, and J. Ahrens. Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling. InProceedings of the 30th International Symposium on High-Performance Parallel and Distributed C...

    2021

  13. [21]

    Köpp and T

    W. Köpp and T. Weinkauf. Temporal Merge Tree Maps: A Topology- Based Static Visualization for Temporal Scalar Data.IEEE Transactions on Visualization and Computer Graphics, 29(1):1157–1167, 2023. doi: 10 .1109/TVCG.2022.3209387 2

    2023

  14. [22]

    F. Lan, B. Gamelin, L. Yan, J. Wang, B. Wang, and H. Guo. Topological Characterization and Uncertainty Visualization of Atmospheric Rivers. Computer Graphics Forum, 43(3):e15084, 2024. doi: 10.1111/cgf.15084 8

    2024 doi

  15. [23]

    S. Li, S. Di, K. Zhao, X. Liang, Z. Chen, and F. Cappello. Resilient Error- Bounded Lossy Compressor for Data Transfer. InSC21: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–14, Nov. 2021. doi: 10.1145/3458817.3476195 2

    2021

  16. [24]

    S. Li, P. Lindstrom, and J. Clyne. Lossy Scientific Data Compression With SPERR. In2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 1007–1017, 2023. doi: 10.1109/IPDPS54959. 2023.00104 2

    2023

  17. [25]

    Y . Li, X. Liang, B. Wang, Y . Qiu, L. Yan, and H. Guo. MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error- Bounded Lossy Compressors.IEEE Transactions on Visualization and Computer Graphics, 31(1):130–140, Jan. 2025. doi: 10.1109/TVCG.2024 ...

    2025 doi

  18. [26]

    Y . Li, M. Xia, X. Liang, B. Wang, R. Underwood, S. Di et al. pMSz: A Distributed Parallel Algorithm for Correcting Morse-Smale Segmentations for Lossy Compression. InProceedings of the 40th IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2026. ...

    2026 doi

  19. [27]

    Liang, S

    X. Liang, S. Di, F. Cappello, M. Raj, C. Liu, K. Ono et al. Toward Feature-Preserving Vector Field Compression.IEEE Transactions on Visualization and Computer Graphics, 29(12):5434–5450, 2023. doi: 10. 1109/TVCG.2022.3214821 2

    2023

  20. [28]

    Liang, S

    X. Liang, S. Di, D. Tao, S. Li, S. Li, H. Guo et al. Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets. In2018 IEEE International Conference on Big Data (Big Data), pp. 438–447, Dec. 2018. doi: 10.1109/BigData.2018.8622520 2

    2018

  21. [29]

    Liang, H

    X. Liang, H. Guo, S. Di, F. Cappello, M. Raj, C. Liu et al. Toward Feature-Preserving 2D and 3D Vector Field Compression. In2020 IEEE Pacific Visualization Symposium (PacificVis), pp. 81–90, 2020. doi: 10. 1109/PacificVis48177.2020.6431 2, 8

    2020

  22. [30]

    Liang, K

    X. Liang, K. Zhao, S. Di, S. Li, R. Underwood, A. M. Gok et al. SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors.IEEE Transactions on Big Data, 9(2):485–498, Apr

  23. [31]

    Lindstrom

    P. Lindstrom. Fixed-Rate Compressed Floating-Point Arrays.IEEE Transactions on Visualization and Computer Graphics, 20(12):2674–2683, Dec. 2014. doi: 10.1109/TVCG.2014.2346458 2

    2014

  24. [32]

    Lindstrom and M

    P. Lindstrom and M. Isenburg. Fast and Efficient Compression of Floating- Point Data.IEEE Transactions on Visualization and Computer Graphics, 12(5):1245–1250, Sept. 2006. doi: 10.1109/TVCG.2006.143 2

    2006 doi

  25. [33]

    J. Liu, S. Di, S. Jin, K. Zhao, X. Liang, Z. Chen et al. Scientific Error- bounded Lossy Compression with Super-resolution Neural Networks. In 2023 IEEE International Conference on Big Data (BigData), pp. 229–236, Dec. 2023. doi: 10.1109/BigData59044.2023.10386682 2

    2023

  26. [34]

    J. Liu, S. Di, K. Zhao, S. Jin, D. Tao, X. Liang et al. Exploring Autoencoder-based Error-bounded Compression for Scientific Data. In 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 294–306, Sept. 2021. ISSN: 2168-9253. doi: 10.1109/Cluster48925. 2021.00034 2

    2021

  27. [35]

    R. G. C. Maack, J. Lukasczyk, J. Tierny, H. Hagen, R. Maciejewski, and C. Garth. Parallel Computation of Piecewise Linear Morse-Smale Segmentations.IEEE Transactions on Visualization and Computer Graphics, 30(4):1942–1955, 2024. doi: 10.1109/TVCG.2023.3261981 3

    1942

  28. [36]

    numpy.fft.fftshift

    NumPy Developers. numpy.fft.fftshift. https://numpy.org/doc/ stable/reference/generated/numpy.fft.fftshift.html, 2026. 4

    2026

  29. [37]

    Piccolotto, M

    N. Piccolotto, M. Bögl, and S. Miksch. Visual Parameter Space Exploration in Time and Space.Computer Graphics Forum, 42(6):e14785,

  30. [38]

    Potter, A

    K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V . Pascucci et al. Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data. In2009 IEEE International Conference on Data Mining Workshops, pp. 233–240, 2009. doi: 10.1109/ICDMW.2009.55 3

    2009 doi

  31. [39]

    C. Ren, R. Underwood, S. Di, E. Kutay, Z. Lukic, A. Yener et al. FFCz: Fast Fourier Correction for Spectrum-Preserving Lossy Compression of Scientific Data. InProceedings of the 40th IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2026. Accepted...

    2026 doi

  32. [40]

    doi: 10.1111/cgf.14785 3

    doi

  33. [41]

    Schneider, D

    B. Schneider, D. Jäckle, F. Stoffel, A. Diehl, J. Fuchs, and D. Keim. Integrating Data and Model Space in Ensemble Learning by Visual Analytics.IEEE Transactions on Big Data, 7(3):483–496, 2021. doi: 10.1109/TBDATA.2018.2877350 3

    2021

  34. [42]

    Sedlmair, C

    M. Sedlmair, C. Heinzl, S. Bruckner, H. Piringer, and T. Möller. Visual Parameter Space Analysis: A Conceptual Framework.IEEE Transactions on Visualization and Computer Graphics, 20(12):2161–2170, 2014. doi: 10.1109/TVCG.2014.2346321 3

    2014

  35. [43]

    Sasaki, K

    N. Sasaki, K. Sato, T. Endo, and S. Matsuoka. Exploration of Lossy Compression for Application-Level Checkpoint/Restart. In2015 IEEE International Parallel and Distributed Processing Symposium, pp. 914– 922, May 2015. doi: 10.1109/IPDPS.2015.67 2

    2015 doi

  36. [44]

    Sundararajan.The Discrete Fourier Transform: Theory, Algorithms and Applications

    D. Sundararajan.The Discrete Fourier Transform: Theory, Algorithms and Applications. World Scientific, 2001. 3

    2001

  37. [45]

    D. Tao, S. Di, Z. Chen, and F. Cappello. Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization. In2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 1129–1139, M...

    2017 doi

  38. [46]

    Soler, M

    M. Soler, M. Plainchault, B. Conche, and J. Tierny. Topologically Controlled Lossy Compression. In2018 IEEE Pacific Visualization Symposium (PacificVis), pp. 46–55, 2018. doi: 10.1109/PacificVis.2018. 00015 2

    2018 doi

  39. [47]

    Underwood, V

    R. Underwood, V . Malvoso, J. C. Calhoun, S. Di, and F. Cappello. Productive and Performant Generic Lossy Data Compression with LibPressio. In2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7), pp. 1–10, Nov. 2021. doi: 10.1109/DRB...

    2021

  40. [48]

    M. P. van Daalen, J. Schaye, C. M. Booth, and C. Dalla Vecchia. The effects of galaxy formation on the matter power spectrum: a challenge for precision cosmology.Monthly Notices of the Royal Astronomical Society, 415(4):3649–3665, 2011. doi: 10.1111/j.1365-2966.2011.18981.x 3

    2011

  41. [49]

    D. Tao, S. Di, H. Guo, Z. Chen, and F. Cappello. Z-checker: A framework for assessing lossy compression of scientific data.The International Journal of High Performance Computing Applications, 33(2):285–303, Mar. 2019. doi: 10.1177/1094342017737147 2, 3, 12

    2019 doi

  42. [50]

    Wickham.ggplot2: Elegant Graphics for Data Analysis

    H. Wickham.ggplot2: Elegant Graphics for Data Analysis. Springer- Verlag New York, 2016. 2

    2016

  43. [51]

    M. Xia, S. Di, F. Cappello, P. Jiao, K. Zhao, J. Liu et al. Preserving Topological Feature with Sign-of-Determinant Predicates in Lossy Compression: A Case Study of Vector Field Critical Points. In2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 4979–499...

    2024

  44. [52]

    J. Wang, S. Hazarika, C. Li, and H.-W. Shen. Visualization and Visual Analysis of Ensemble Data: A Survey.IEEE Transactions on Visualization and Computer Graphics, 25(9):2853–2872, 2019. doi: 10.1109/TVCG. 2018.2853721 3

    2019

  45. [53]

    L. Yan, X. Liang, H. Guo, and B. Wang. TopoSZ: Preserving Topology in Error-Bounded Lossy Compression.IEEE Transactions on Visualization and Computer Graphics, 30(1):1302–1312, 2024. doi: 10.1109/TVCG. 2023.3326920 2, 8

    2024

  46. [54]

    X. Yu, S. Di, K. Zhao, J. Tian, D. Tao, X. Liang et al. Ultrafast Error-bounded Lossy Compression for Scientific Datasets. InProceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing, HPDC ’22, pp. 159–171. Association for Computing...

    2022

  47. [55]

    M. Xia, B. Wang, Y . Li, P. Jiao, X. Liang, and H. Guo. TspSZ: An Efficient Parallel Error-Bounded Lossy Compressor for Topological Skeleton Preservation. In2025 IEEE 41st International Conference on Data Engineering (ICDE), pp. 3682–3695, 2025. doi: 10.1109/ICDE65448. 2025.00275 2

    2025

  48. [56]

    K. Zhao, S. Di, X. Liang, S. Li, D. Tao, Z. Chen et al. Significantly Improving Lossy Compression for HPC Datasets with Second-Order Prediction and Parameter Optimization. InProceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing,...

    2020

  49. [57]

    K. Zhao, S. Di, D. Perez, X. Liang, Z. Chen, and F. Cappello. MDZ: An Efficient Error-bounded Lossy Compressor for Molecular Dynamics. In 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 27–40, May 2022. doi: 10.1109/ICDE53745.2022.00007 2

    2022

  50. [58]

    K. Zhao, S. Di, M. Dmitriev, T.-L. D. Tonellot, Z. Chen, and F. Cappello. Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation. In2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1643–1654, Apr. 2021. doi: 10.110...

    2021

  51. [59]

    2 A WORKFLOWCOMPARISON This section summarizes how FZ-VIS differs from existing tools in compression workflow and QoI-aware analysis capabilities

    Zstandard.http://www.zstd.net, 2024. 2 A WORKFLOWCOMPARISON This section summarizes how FZ-VIS differs from existing tools in compression workflow and QoI-aware analysis capabilities. Table 1 complements the discussion in Section 2 and Section 5. While LibPressio [47] offers a...

    2024

  52. [61]

    Zhou and M

    J. Zhou and M. Takatsuka. Automatic Transfer Function Generation Using Contour Tree Controlled Residue Flow Model and Color Harmonics.IEEE Transactions on Visualization and Computer Graphics, 15(6):1481–1488,

  53. [2009]

    doi: 10.1109/TVCG.2009.120 2

    2009 doi

  54. [2023]

    doi: 10.1109/TBDATA.2022.3201176 2

    2022

  55. [2025]

    doi: 10.1109/TVCG.2025.3567054 2

    2025

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.