Data in volume form consumes an extraordinary amount of storage space. For efficient storage and transmission of such data, compression algorithms are imperative. However, most volumetric datasets are used in biomedicine and other scientific applications where lossy compression is unacceptable. We present a lossless data-compression algorithm which, being oriented specifically for volume data, achieves greater compression performance than generic compression algorithms that are typically available on modern computer systems. Our algorithm is a combination of differential pulse-code modulation (DPCM) and Huffman coding and results in compression of around 50% for a set of volume data files. I. Introduction Compression for efficient storage and transmission of digital data has become routine as the application of such data has grown. Several common datacompression programs are readily available on many computers to fight the burgeoning demand for storage space. These programs are typica...

Results overview: First, a volumetric scalar data set of size 256 3 requiring 16 MB is shown. Second, the hierarchically encoded data set (0.78 MB) is directly rendered using programmable graphics hardware. Third, one time step (256 3) of a 1.4 GB shock wave simulation is shown. Fourth, the same time step is directly rendered out of a compressed sequence of 70 MB. Rendering the data sets to a 512 2 viewport runs at 11 and 24 fps, respectively, on an ATI 9700. A survey of graphics developers on the issue of texture mapping hardware for volume rendering would most likely find that the vast majority of them view limited texture memory as one of the most serious drawbacks of an otherwise fine technology. In this paper, we propose a compression scheme for static and time-varying volumetric data sets based on vector quantization that allows us to circumvent this limitation. We describe a hierarchical quantization scheme that is based on a multiresolution covariance analysis of the original field. This allows for the efficient encoding of large-scale data sets, yet providing a mechanism to exploit temporal coherence in non-stationary fields. We show, that decoding and rendering the compressed data stream can be done on the graphics chip using programmable hardware. In this way, data transfer between the CPU and the graphics processing unit (GPU) can be minimized thus enabling flexible and memory efficient real-time rendering options. We demonstrate the effectiveness of our approach by demonstrating interactive renditions of Gigabyte data sets at reasonable fidelity on commodity graphics hardware.

In order to achieve greater realism, we are applying anatomical and physiological principles to model and animate animals. Underlying components represent bones, muscles, and soft tissue. For speed and simplicity, these are modeled from ellipsoids. Muscles stretch across joints, and their orientations, sizes, and shapes change during joint motion. A polygonal skin is automatically generated from the underlying structures. The skin mesh adjusts itself to changes in position under the influence of neighboring skin points and connections to the underlying anatomy. Much of the process is automated, but under the control of user-defined parameters. Manipulation and animation of these models occur at comfortable interactive speeds on graphics workstations. Keywords: computer graphics, computer animation, computer modeling, animal and skin modeling. Electronic Version: This is an electronic version of a paper to appear in IEEE Computer Graphics and Applications during the spring of 1997. It...

Image compression is the process of reducing the number of bits required to represent an image. Vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the compression step. Recent work has used vector quantization both to simplify image processing tasks -- such as enhancement, classification, halftoning, and edge detection -- and to reduce the computational complexity by performing them simultaneously with the compression. After briefly reviewing the fundamental ideas of vector quantization, we present a survey of vector quantization algorithms that perform image processing. 1 Introduction Data compression is the mapping of a data set into a bit stream to decrease the number of bits required to represent the data set. With data compression, one can st...

Volumetric data sets require enormous storage capacity even at moderate resolution levels. The excessive storage demands not only stress the capacity of the underlying storage and communications systems, but also seriously limit the speed of volume rendering due to data movement and manipulation. A novel volumetric data visualization scheme is proposed and implemented in this work that renders 2D images directly from compressed 3D data sets. The novelty of this algorithm is that rendering is performed on the compressed representation of the volumetric data without pre-decompression. As a result, the overheads associated with both data movement and rendering processing are significantly reduced. The proposed algorithm generalizes previously proposed whole-volume frequency-domain rendering schemes by first dividing the 3D data set into subcubes, transforming each subcube to a frequency-domain representation, and applying the Fourier Projection Theorem to produce the projected 2D images a...

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. 1

This research is the first application of fractal compression to volumetric data. The various components of the fractal image compression method extend simply and directly to the volumetric case. However, the additional dimension increases the already high time complexity of the fractal technique, requiring the application of sophisticated volumetric block classification and search schemes to operate at a feasible rate. Numerous experiments over the many parameters of fractal volume compression show it to perform competitively against other volume compression methods, surpassing vector quantization and approaching the discrete cosine transform. Keywords: Data compression, fractal, iterated function system, volume visualization. 1 Introduction The problem of managing extremely large data sets often arises in applications employing volumetric data. This has prompted research in new techniques for economizing both storage space and processing time. Data compression techniques reduce th...

Numerical simulation of physical phenomena is now an accepted way of scientific inquiry. However, the field is still evolving, with a profusion of new solution and grid-generation techniques being continuously proposed. Concurrent and retrospective visualization are being used to validate the results, compare them among themselves and with experimental data, and browse through large scientific databases. There exists a need for representation schemes which allow access of structures in an increasing order of smoothness (or decreasing order of significance). We describe our methods on datasets obtained from curvilinear grids. Our target application required visualization of a computational simulation performed on a very remote supercomputer. Since no grid adaptation was performed, it was not deemed necessary to simplify or compress the grid. In essence, we treat the solution as if it were in the computational domain. Inherent to the identification of significant structures is determining the location of the scale coherent structures and assigning saliency values to them [22], [23]. Scale coherent structures are obtained as a result of combining the coefficients of a wavelet transform across scales. The result of this operation is a correlation mask that delineates regions containing significant structures. A spatial subdivision (e.g., octree) is used to delineate regions of interest. The mask values in these subdivided regions are used as a measure of information content. Later, another wavelet transform is conducted within each subdivided region and the coefficients are sorted based on a perceptual function with bandpass characteristics. This allows for ranking of structures based on the order of significance, giving rise to an adaptive and embedded representation scheme. We demonstrate our methods on two datasets from computational field simulations. Essentially, we show

Designing Optimal Parallel Volume Rendering Algorithms by Craig Michael Wittenbrink Chairperson of the Supervisory Committee: Professor Arun K. Somani Department of Electrical Engineering and Department of Computer Science and Engineering Volume rendering is a method for visualizing volumes of sampled data such as CT, MRI, and finite element simulations. Visualization of medical and simulation data improves understanding and interpretation, but volume rendering is expensive and each frame takes from minutes to hours to calculate. Parallel computers provide the potential for interactive volume rendering, but parallel algorithms have not matched sequential algorithm 's features, nor have they provided the speedup possible. I introduce a methodology to control the complexity in designing parallel algorithms, and apply this methodology to volume rendering. The result is parallel algorithms with all of the features of sequential ones that deliver the promise of parallelism. My algorithms ...

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