We propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The weights can be assigned with a simple band-based approach, or with an efficient pixel-based approach that reduces ringing artifacts. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.

我们提出了一种用于颜色编码可视化的图像空间对比度增强方法。通过感知引导的方法可以增强图像的对比度，该方法可通过虚拟观看距离的单个直观参数与用户进行交互。为此，我们分析了输入图像的多尺度对比度模型，并在虚拟观察距离下测试了所有尺度的带通图像的可见性。通过使用空间视觉的阈值模型调整带通图像的权重，此基于图像的方法可增强对比度，以补偿因在一定距离下观看图像而导致的对比度损失。用户可以使用过度补偿来进一步强调彩色图像中的相关特征。可以使用基于频带的简单方法来分配权重，或采用有效的基于像素的方法来减少振铃伪像。该方法是有效的，并且可以集成到任何可视化工具中，因为它是基于图像的通用后处理技术。使用高度多样化的数据集，我们展示了广泛的典型可视化中感知补偿的有用性。