Visual Analysis of Popping in Progressive Visualization

E. Waterink, J. Kosinka, S. Frey
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引用次数: 1

Abstract

Progressive visualization allows users to examine intermediate results while they are further refined in the background. This makes them increasingly popular when dealing with large data and computationally expensive tasks. The characteristics of how preliminary visualizations evolve over time are crucial for efficient analysis; in particular unexpected disruptive changes between iterations can significantly hamper the user experience. This paper proposes a visualization framework to analyze the refinement behavior of progressive visualization. We particularly focus on sudden significant changes between the iterations, which we denote as popping artifacts, in reference to undesirable visual effects in the context of level of detail representations in computer graphics. Our visualization approach conveys where in image space and when during the refinement popping artifacts occur. It allows to compare across different runs of stochastic processes, and supports parameter studies for gaining further insights and tuning the algorithms under consideration. We demonstrate the application of our framework and its effectiveness via two diverse use cases with underlying stochastic processes: adaptive image space sampling, and the generation of grid layouts. CCS Concepts • Human-centered computing → Visualization design and evaluation methods;
渐进式可视化中弹出的可视化分析
渐进式可视化允许用户检查中间结果,同时在后台进一步细化。这使得它们在处理大数据和计算成本高的任务时越来越受欢迎。初步可视化如何随时间演变的特征对于有效分析至关重要;特别是在迭代之间意外的破坏性更改会严重妨碍用户体验。本文提出了一种可视化框架来分析渐进式可视化的细化行为。我们特别关注迭代之间突然的重大变化,我们将其表示为弹出的工件,参考计算机图形中细节表示级别上下文中不希望看到的视觉效果。我们的可视化方法传达了在图像空间中的位置以及在细化过程中出现弹出工件的时间。它允许在随机过程的不同运行之间进行比较,并支持参数研究,以获得进一步的见解和调整正在考虑的算法。我们通过两个不同的用例展示了我们的框架的应用及其有效性,这些用例具有潜在的随机过程:自适应图像空间采样和网格布局的生成。•以人为本的计算→可视化设计与评价方法;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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