Histogram spectra for multivariate time-varying volume LOD selection

Level of detail techniques are widely applied to minimize sampling error subject to working set size constraints. Typical large data sets being produced today have many variables sampled across time-varying volumes. Visualization of these multivariate volumes is commonly phrased in terms of conditional expressions such as “show variable A where variable B is between B₁ and B₂.” The bounds, B₁ and B₂, tend to be specified during the interactive portion of the workflow. Thus, to maximize quality over the salient interval, level of detail selection should also be interactive. We introduce the concept of histogram spectra to quickly and compactly quantify the statistical sensitivity of volumes to sampling. Salient interval volumes of one or more variables are used to select which parts of the histogram spectra are important. The level of detail selection problem, over a time-varying, multivariate, multiresolution volume, is then posed as an integer programming problem using the histogram spectra. We propose an efficient solution enabling interactive LOD selection on large, out-of-core volumes and show its efficacy on two real data sets from different problem domains.