发表机构
Shandong University; Ocean University of China; Laoshan Laboratory（山东大学; 中国海洋大学; 崂山实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出数据感知的颜色映射评估公式，将可视化建模为数据空间到感知颜色空间的复合映射，补充判别力、均匀性等度量的数据感知版本，并验证其有效性及在优化中的应用。
AI 中文摘要
连续颜色映射被广泛用于可视化标量场，其质量通常通过判别力和均匀性等度量来评估。现有度量主要表征颜色映射本身的固有感知属性，在很大程度上独立于底层数据分布。然而，在实践中，用户感知并非仅来自颜色映射本身，而是来自通过颜色映射映射数据值所生成的可视化。用户实际体验到的感知差异取决于颜色映射和底层数据两者的共同作用。我们提出了一种数据感知的公式，以补充现有的数据无关的颜色映射评估方法。我们不是孤立地分析颜色映射，而是将颜色编码的可视化建模为从数据的空间域到感知颜色空间的复合映射。这种方法为已有的度量（包括判别力、均匀性和平滑性）提供了数据感知的对应度量，并增加了感知各向异性和退化等额外度量。我们针对现有的数据无关框架和感知研究的实证结果验证了所提出的度量，将公式扩展到二维颜色映射，并展示了其在颜色映射优化中的集成。提供了一个交互式系统，用于在不同数据分布下探索颜色映射评估。结果表明，我们的公式为数据感知的颜色映射评估和设计提供了原则性的基础。
英文摘要
Continuous colormaps are widely used to visualize scalar fields, and their quality is typically evaluated using measures such as discriminative power and uniformity. Existing measures primarily characterize the intrinsic perceptual properties of the colormap itself, largely independent of the underlying data distribution. In practice, however, user perception arises not from the colormap alone but from the visualization generated by mapping data values through the colormap. The perceptual differences that users actually experience depend jointly on the colormap and the underlying data. We propose a data-aware formulation that complements existing data-independent colormap assessment approaches. Rather than analyzing the colormap in isolation, we model the color-encoded visualization as a composite mapping from the spatial domain of the data to perceptual color space. This approach yields data-aware counterparts of established measures, including discriminative power, uniformity, and smoothness, as well as additional measures such as perceptual anisotropy and degeneracy. We validate the proposed measures against both the existing data-independent framework and empirical results from perceptual studies, extend the formulation to 2D colormaps, and demonstrate its integration into colormap optimization. An interactive system is provided for exploring colormap assessment under varying data distributions. Results demonstrate that our formulation offers a principled foundation for data-aware colormap assessment and design.
Comments11 pages, 9 figures, IEEE VIS 2026 conference full paper
