Linesmooth: An Analytical Framework For Evaluating The Effectiveness Of Smoothing Techniques On Line Charts

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.

Paul Rosen, and Ghulam Jilani Quadri. Linesmooth: An Analytical Framework For Evaluating The Effectiveness Of Smoothing Techniques On Line Charts. IEEE Transactions on Visualization and Computer Graphics (IEEE VAST), 2021.

@article{rosen2021linesmooth,
  title = {LineSmooth: An Analytical Framework for Evaluating the Effectiveness of
    Smoothing Techniques on Line Charts},
  author = {Rosen, Paul and Quadri, Ghulam Jilani},
  journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE VAST)},
  year = {2021},
  note = {textit{Presented at IEEE VIS 2020.}},
  abstract = {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.}
}