什么使一个可视化图像复杂?
IEEE transactions on visualization and computer graphics
|November 21, 2025
概括
研究人员使用客观指标量化数据可视化中的视觉复杂性. 他们发现,角落,鲜明的颜色和文本与墨水的比率等元素显著影响了复杂的可视化.
科学领域:
- 数据可视化 数据可视化
- 人与计算机的交互
- 感知科学 感知科学
背景情况:
- 感知到的视觉复杂性 (VC) 对于有效的数据可视化至关重要.
- 需要客观的指标来量化VC并了解用户的感知.
- 现有的指标可能无法完全捕捉各种可视化中虚拟货币的细微差别.
研究的目的:
- 在数据可视化中使用客观的基于图像的指标来调查感知到的视觉复杂性.
- 为了评估人为得分的VC与各种计算指标的对齐.
- 开发和验证一个可量化的模型来预测被认为的VC.
主要方法:
- 进行了一项大型众包实验,349名参与者为VC评价了1800张可视化图像.
- 分析了12个基于图像的指标,包括基于像素,杂乱,颜色,形状和基于对象的新型指标 (有意义的颜色数 (MeC) 和文本与墨水比 (TiR)).
- 开发了一个基于VisComplexity2K数据集的量化模型.
主要成果:
- 低层特征 (边缘,角落) 和高层元素 (鲜明的颜色) 都显著影响感知到的VC.
- 特征拥堵是连续色彩/纹理可视化的强有力的预测因素,而边缘密度则是节点链路图的有效因素.
- 对于文本与墨水比率 (TiR) 观察到一个钟曲线关系,表明一个最佳的文本密度来减少复杂性.
结论:
- 客观指标,包括MeC和TiR等新指标,可以有效地预测数据可视化中的视觉复杂性.
- 了解低级和高级特征的相互作用,为可视化设计提供了洞察力.
- 开发的模型为感知VC提供了可解释的,基于指标的解释,弥合了计算方法和人类感知.
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