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dc.contributor.authorBaker, Allisonen_US
dc.contributor.authorHammerling, Doriten_US
dc.contributor.authorTurton, Tereceen_US
dc.contributor.editorGleicher, Michael and Viola, Ivan and Leitte, Heikeen_US
dc.description.abstractApplying lossy data compression to climate model output is an attractive means of reducing the enormous volumes of data generated by climate models. However, because lossy data compression does not exactly preserve the original data, its application to scientific data must be done judiciously. To this end, a collection of measures is being developed to evaluate various aspects of lossy compression quality on climate model output. Given the importance of data visualization to climate scientists interacting with model output, any suite of measures must include a means of assessing whether images generated from the compressed model data are noticeably different from images based on the original model data. Therefore, in this work we conduct a forcedchoice visual evaluation study with climate model data that surveyed more than one hundred participants with domain relevant expertise. In addition to the images created from unaltered climate model data, study images are generated from model data that is subjected to two different types of lossy compression approaches and multiple levels (amounts) of compression. Study participants indicate whether a visual difference can be seen, with respect to the reference image, due to lossy compression effects. We assess the relationship between the perceptual scores from the user study to a number of common (full reference) image quality assessment (IQA) measures, and use statistical models to suggest appropriate measures and thresholds for evaluating lossily compressed climate data. We find the structural similarity index (SSIM) to perform the best, and our findings indicate that the threshold required for climate model data is much higher than previous findings in the literature.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectE.4 [Coding and Information Theory]
dc.subjectData Compaction and Compression
dc.subjectI.5.2 [Design Methodology]
dc.subjectFeature evaluation
dc.subjectH.1.2 [User/Machine Systems]
dc.subjectHuman factors
dc.titleEvaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Dataen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersSpatial Data Applications

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  • 38-Issue 3
    EuroVis 2019 - Conference Proceedings

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