Quality Metric for LOD Volume Rendering

For large volume visualization, an image-based quality metric is difficult to incorporate for level-of-detail selection and rendering without sacrificing the interactivity. This is because it is usually time-consuming to update view-dependent information as well as adjust to transfer function changes. We introduce an image-based level-of-detail selection algorithm for interactive visualization of large volumetric data. The design of our quality metric is based on an efficient way to evaluate the contribution of multiresolution data blocks to the final image. To ensure real-time update of the quality metric and interactive level-of-detail decisions, we propose a summary table scheme in response to run-time transfer function changes, and a GPU-based solution for visibility estimation. Experimental results on large scientific and medical data sets demonstrate the effectiveness and efficiency of our algorithm. This work has been published in IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 1, 2006. [pdf]

Credits: This work was supported by NSF ITR grant ACI-0325934, DOE Early Career Principal Investigator Award DE-FG02-03ER25572, NSF Career Award CCF-0346883, and Oak Ridge National Laboratory Contract 400045529. The RMI data set is courtesy of Mark A. Duchaineau at Lawrence Livermore National Laboratory.