Liu Shuqi, Tao Mingtian, Huang Yifei, Wang Changbo, Li Chenhui
IEEE Trans Vis Comput Graph. 2024 Jul;30(7):3089-3103. doi: 10.1109/TVCG.2022.3226218. Epub 2024 Jun 27.
Color has been widely used to encode data in all types of visualizations. Effective color palettes contain discriminable and harmonious colors, which allow information from visualizations to be accurately and aesthetically conveyed. However, predefined color palettes not only lack the flexibility of custom color palette generation but also ignore the context in which the visualizations are used. Designing an effective color palette is a time-consuming and challenging process for users, even experts. In this work, we propose the generation of an image-based visualization color palette to exploit the human perception of visually appealing images while considering visualization cognition. By analyzing color palette constraints, including harmony, discrimination, and context, we propose an image-driven color generation method. We design a color clustering method in the saliency-hue plane based on visual importance detection and then select the palette based on the visualization color constraints. In addition, we design two color optimization and assignment strategies for visualizations of different data types. Evaluations through numeric indicators and user experiments demonstrate that the palettes predicted by our method are visually related to the original images and are aesthetically pleasing, supporting diverse visualization contexts and data types in practical applications.
颜色已被广泛用于各类可视化中对数据进行编码。有效的调色板包含可区分且协调的颜色，这使得可视化中的信息能够被准确且美观地传达。然而，预定义的调色板不仅缺乏生成自定义调色板的灵活性，还忽略了可视化使用的上下文。对于用户甚至专家而言，设计一个有效的调色板都是一个耗时且具有挑战性的过程。在这项工作中，我们提出生成基于图像的可视化调色板，以利用人类对视觉上吸引人的图像的感知，同时考虑可视化认知。通过分析调色板的约束条件，包括协调性、可区分性和上下文，我们提出了一种图像驱动的颜色生成方法。我们基于视觉重要性检测在显著性 - 色调平面中设计了一种颜色聚类方法，然后根据可视化颜色约束选择调色板。此外，我们针对不同数据类型的可视化设计了两种颜色优化和分配策略。通过数值指标和用户实验进行的评估表明，我们的方法预测的调色板在视觉上与原始图像相关且美观，在实际应用中支持多种可视化上下文和数据类型。
