LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts
LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts
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DOI：
10.1109/tvcg.2020.3030421
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发表时间：
2020-07
影响因子：
5.2
通讯作者：
P. Rosen;Ghulam Jilani Quadri
中科院分区：
文献类型：
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作者：
P. Rosen;Ghulam Jilani Quadri
我们提出了一个全面的框架，用于评估各种可视化分析任务下的折线图平滑方法。折线图通常用于可视化一系列数据样本。当样本数量很大或数据有噪声时，可以应用平滑来使信号更明显。然而，有各种各样的平滑技术可用，每种技术的有效性取决于数据的性质和手头的可视化分析任务。到目前为止，可视化社区缺乏一个总结的工作，分析和分类的各种平滑方法。在本文中，我们建立了一个框架，基于8个与8个低级视觉分析任务相关联的线条平滑有效性的度量。然后，我们分析了12种方法，来自4个常用的线图平滑等级滤波器，卷积滤波器，频域滤波器和子采样。结果表明，虽然没有一种方法是理想的所有情况下，某些方法，如高斯滤波器和基于拓扑的子采样，一般表现良好。其他方法，如低通CUTOFF滤波器和Douglas-Peucker子采样，对于特定的视觉分析任务表现良好。几乎同样重要的是，我们的框架表明，几种方法，包括常用的CANORM子采样，产生低质量的结果，因此，应该避免，如果可能的话。
We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. Line charts are commonly used to visualize a series of data samples. When the number of samples is large, or the data are noisy, smoothing can be applied to make the signal more apparent. However, there are a wide variety of smoothing techniques available, and the effectiveness of each depends upon both nature of the data and the visual analytics task at hand. To date, the visualization community lacks a summary work for analyzing and classifying the various smoothing methods available. In this paper, we establish a framework, based on 8 measures of the line smoothing effectiveness tied to 8 low-level visual analytics tasks. We then analyze 12 methods coming from 4 commonly used classes of line chart smoothing-rank filters, convolutional filters, frequency domain filters, and subsampling. The results show that while no method is ideal for all situations, certain methods, such as Gaussian filters and TOPOLOGY-based subsampling, perform well in general. Other methods, such as low-pass CUTOFF filters and Douglas-peucker subsampling, perform well for specific visual analytics tasks. Almost as importantly, our framework demonstrates that several methods, including the commonly used UNIFORM subsampling, produce low-quality results, and should, therefore, be avoided, if possible.