Advanced Quality Metrics in Information Visualization and Scientific Visualization

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Quality metrics are a promising concept for the automatic analysis of visualizations from high-dimensional data. In order to completely visualize a high-dimensional data set, a large number of visualizations are required. Just a subset of them shows relevant structures of the data and thus just a subset of them is required to be seen by the user. The idea of quality metrics is to automatically detect this subset of good visualizations. For this, they mimic the visual perception system. A couple of quality metrics are known aiming mostly at the analysis of bi-variate discrete visualizations of high-dimensional data. This project extends the traditional approach of quality metrics in three ways. The concept of quality metrics will be extended to nonlinear embeddings in multivariate projections, it will be extended to non-discrete visualizations (aka continuous visualizations), and it will be extended in order to measure the reliability of quality metrics. As the conceptional extensions are mutually related, we propose their treatment within one project.