Visualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme.

使用诸如颜色，形状，大小和方向之类的视觉属性将数据可视化为图表具有挑战性。特别是，大数据集要求图形显示是分析中的重要步骤。为了实现理解，通常需要同时显示不同的属性。在这项工作中，提出并评估了针对高维数据的可理解的双变量，感知优化的可视化方案。它可用于在单个图表中显示倍数变化和置信度值。可视化方案由两部分组成：统一，对称的双面色标和面片网格表示。二十五个观察员与标准RGB标尺相比，对双面色标的均匀性和对称性进行了评估。此外，