Compression and Accelerated Rendering of Time-Varying Volume Data
نویسندگان
چکیده
Visualization of time-varying volumetric data sets, which may be obtained from numerical simulations or sensing instruments, provides scientists insights into the detailed dynamics of the phenomenon under study. This paper describes our study of a coherent solution based on quantization coupled with octree and difference encoding, and adaptive rendering for efficient visualization of timevarying volumetric data. Quantization is used to attain voxel-level compression and may have a significant influence on the performance of the subsequent encoding and visualization steps. Octree encoding is used for spatial domain compression, and difference encoding for temporal domain compression. In essence, neighboring voxels may be fused into macro voxels if they have similar values, and subtrees at consecutive time steps may be merged if they are identical. The software rendering process is tailored according to the tree structures and the volume visualization process. With the tree representation, selective rendering may be performed very efficiently. Additionally, the I/O costs are reduced. With these combined savings, a higher level of user interactivity is achieved. We have studied a variety of time-varying volume data sets, performed encoding based on data statistics, and optimized the rendering calculations wherever possible. Preliminary tests on workstations have shown in many cases tremendous reduction by as high as 90% in both storage space and inter-frame delay when compared to direct rendering of the raw data.
منابع مشابه
Time-varying Volume Compression in Spatio-temporal Domain
Data compression is always needed in large-scale time-varying volume visualization. In some recent application cases, the compression method is also required to provide a low-cost decompression process. In this paper, we propose a compression scheme for large-scale time-varying volume data using the spatio-temporal features. With this compression scheme, we are able to provide a proper compress...
متن کاملFeature-Enhanced Visualization of Multidimensional, Multivariate Volume Data Using Non-photorealistic Rendering Techniques
This paper presents a set of feature enhancement techniques coupled with hardware-accelerated nonphotorealistic rendering for generating more perceptually effective visualization of multidimensional, multivariate volume data, such as those obtained from typical computational fluid dynamics simulations. For time-invariant data, one or more variables are used to either highlight important feature...
متن کاملA decompression pipeline for accelerating out-of-core volume rendering of time-varying data
This paper presents a decompression pipeline capable of accelerating out-of-core volume rendering of time-varying scalar data. Our pipeline is based on a twostage compression method that cooperatively uses the CPU and GPU (graphics processing unit) to transfer compressed data entirely from the storage device to the video memory. This method combines two different compression algorithms, namely ...
متن کاملA Hardware-Assisted Scalable Solution for Interactive Volume Rendering of Time-Varying Data
We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amo...
متن کاملAn adaptive framework for visualizing unstructured grids with time-varying scalar fields
Interactive visualization of time-varying volume data is essential for many scientific simulations. This is a challenging problem since this data is often large, can be organized in different formats (regular or irregular grids), with variable instances of time (from hundreds to thousands) and variable domain fields. It is common to consider subsets of this problem, such as time-varying scalar ...
متن کامل