high performance computing on graphics processing units: hgpu.org

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 to adjust to transfer function changes. In this paper, 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 runtime 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