Fluid Data Compression and ROI Detection Using Run Length Method

AbstractIt is difficult to carry out visualization of the large-scale time-varying data directly, even with the supercomputers. Data compression and ROI (Region of Interest) detection are often used to improve efficiency of the visualization of numerical data. It is well known that the Run Length encoding is a good technique to compress the data where the same sequence appeared repeatedly, such as an image with little change, or a set of smooth fluid data. Another advantage of Run Length encoding is that it can be applied to every dimension of data separately. Therefore, the Run Length method can be implemented easily as a parallel processing algorithm. We proposed two different Run Length based methods. When using the Run Length method to compress a data set, its size may increase after the compression if the data does not contain many repeated parts. We only apply the compression for the case that the data can be compressed effectively. By checking the compression ratio, we can detect ROI. The effectiveness and efficiency of the proposed methods are demonstrated through comparing with several existing compression methods using different sets of fluid data

Previewing Volume Decomposition Through Optimal Viewpoints ∗

Understanding a volume dataset through a 2D display is a complex task because it usually contains multi-layered inner structures that inevitably cause undesirable overlaps when projected onto the display. This requires us to identify feature subvolumes embedded in the given volume and then visualize them on the display so that we can clarify their relative positions. This article therefore introduces a new feature-driven approach to previewing volumes that respects both the 3D nested structures of the feature subvolumes and their 2D arrangement in the projection by minimizing their occlusions. The associated process begins with tracking the topological transitions of isosurfaces with respect to the scalar field, in order to decompose the given volume dataset into feature components called interval volumes while extracting their nested structures. The volume dataset is then projected from the optimal viewpoint that archives the best balanced visibility of the decomposed components. The position of the optimal viewpoint is updated each time when we peel off an outer component with our interface by calculating the sum of the viewpoint optimality values for the remaining components. Several previewing examples are demonstrated to illustrate that the present approach can offer an effective means of traversing volumetric inner structures both in an interactive and automatic fashion with the interface. 1998 ACM Subject Classification I.3.8 [Computer Graphics]: Application