Statistically-weighted visualization hierarchies.

We are beginning to see an overload in the amount of information packed into a given visualization.

In many cases, it is no longer possible to look at a single level of detail and obtain from it the answers we are looking for.

This problem is especially relevant to datasets of high dimensionality.

Not only does it become difficult to hone in on a particular dimension of possible interest, but even more difficult to find and understand the relationships among them.

In traditional computer graphics aimed at 3D rendering, varying orders of magnitude have traditionally been addressed by texture hierarchies known as MIP Maps.

These hierarchies are extremely fast and provide a seamless transition from one level of detail to the next.

Unfortunately, this approach does not carry over to textures full of scientific data.

Instead, such an approach introduces a series of errors which not only misrepresent and corrupt the underlying data as visible to the viewer, but hide interesting features which warrant further investigation.

We propose an alternative hierarchical approach, using statistical analysis to generate more representative macroscopic views of extremely high detailed data fields.

Several variations of this approach are examined, showing that hierarchies generated strictly from base level data are superior, especially when used in conjunction with error diffusion.

Additionally we provide multiple example metrics as possible filter functions for this approach.