A Data-Driven Approach to Hue-Preserving Color-Blending
A Data-Driven Approach to Hue-Preserving Color-Blending
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DOI：
10.1109/tvcg.2012.186
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发表时间：
2012-12
影响因子：
5.2
通讯作者：
L. Kuehne;Joachim Giesen;Zhiyuan Zhang;S. Ha;K. Mueller
中科院分区：
文献类型：
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作者：
L. Kuehne;Joachim Giesen;Zhiyuan Zhang;S. Ha;K. Mueller
颜色映射和半透明分层在许多可视化场景中发挥着重要作用，例如信息可视化和体绘制。颜色和透明度的组合仍然以使用 Porter-Duff 运算符的标准 alpha 合成为主，这可能会导致错误的颜色，从而对可视化产生欺骗性影响。其他更先进的方法也被提出，但问题还远远没有得到解决。在这里，我们提出了这些现有方法的替代方案，专门设计用于避免假色并保持视觉深度排序。我们的方法是数据驱动的，并遵循最近制定的知识辅助可视化（KAV）范式。在基于网络的用户调查中收集的偏好数据用于训练支持向量机模型，以自动预测优化的色调保留混合。我们已将所得模型应用于体积渲染和特定信息可视化技术，即说明性平行坐标图。比较渲染显示出比以前的方法有了显着的改进，因为伪色被完全去除，并且深度排序和混合鲜艳度等重要属性得到了更好的保留。由于定义的数据驱动混合算子的通用性，它也可以轻松集成到其他可视化框架中。
Color mapping and semitransparent layering play an important role in many visualization scenarios, such as information visualization and volume rendering. The combination of color and transparency is still dominated by standard alpha-compositing using the Porter-Duff over operator which can result in false colors with deceiving impact on the visualization. Other more advanced methods have also been proposed, but the problem is still far from being solved. Here we present an alternative to these existing methods specifically devised to avoid false colors and preserve visual depth ordering. Our approach is data driven and follows the recently formulated knowledge-assisted visualization (KAV) paradigm. Preference data, that have been gathered in web-based user surveys, are used to train a support-vector machine model for automatically predicting an optimized hue-preserving blending. We have applied the resulting model to both volume rendering and a specific information visualization technique, illustrative parallel coordinate plots. Comparative renderings show a significant improvement over previous approaches in the sense that false colors are completely removed and important properties such as depth ordering and blending vividness are better preserved. Due to the generality of the defined data-driven blending operator, it can be easily integrated also into other visualization frameworks.