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Visual Reconstructability as a Quality Metric for Flow Visualization
Author(s) -
Jänicke Heike,
Weidner Thomas,
Chung David,
Laramee Robert S.,
Townsend Peter,
Chen Min
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01927.x
Subject(s) - visualization , computer science , metric (unit) , computer vision , artificial intelligence , data visualization , streamlines, streaklines, and pathlines , flow (mathematics) , quality (philosophy) , computer graphics (images) , mathematics , geometry , philosophy , operations management , epistemology , economics , physics , thermodynamics
We present a novel approach for the evaluation of 2D flow visualizations based on the visual reconstructability of the input vector fields. According to this metric, a visualization has high quality if the underlying data can be reliably reconstructed from the image. This approach provides visualization creators with a cost‐effective means to assess the quality of visualization results objectively. We present a vision‐based reconstruction system for the three most commonly‐used visual representations of vector fields, namely streamlines, arrow glyphs, and line integral convolution. To demonstrate the use of visual reconstructability as a quality metric, we consider a selection of vector fields obtained from numerical simulations, containing typical flow features. We apply the three types of visualization to each dataset, and compare the visualization results based on their visual reconstructability of the original vector field.