Volume rendering is a technique for visualizing 3D arrays of sampled data. It has applications in areas such as medical imaging and scientific visualization, but its use has been limited by its high computational expense. Early implementations of volume rendering used brute-force techniques that require on the order of 100 seconds to render typical data sets on a workstation. Algorithms with optimizations that exploit coherence in the data have reduced rendering times to the range of ten seconds but are still not fast enough for interactive visualization applications. In this thesis we present a family of volume rendering algorithms that reduces rendering times to one second. First we present a scanline-order volume rendering algorithm that exploits coherence in both the volume data and the image. We show that scanline-order algorithms are fundamentally more efficient than commonly-used ray casting algorithms because the latter must perform analytic geometry calculations (e.g. intersecting rays with axis-aligned boxes). The new scanline-order algorithm simply streams through the volume and the image in storage order. We describe variants of the algorithm for both parallel and perspective projections and

We present a method for visualizing unsteady flow by displaying its vortices. The vortices are identified by using a vorticity-predictor pressure-corrector scheme that follows vortex cores. The cross-sections of a vortex at each point along the core can be represented by a Fourier series. A vortex can be faithfully reconstructed from the series as a simple quadrilateral mesh, or its reconstruction can be enhanced to indicate helical motion. The mesh can reduce the representation of the flow features by a factor of one thousand or more compared with the volumetric dataset. With this amount of reduction it is possible to implement an interactive system on a graphics workstation to permit a viewer to examine, in three dimensions, the evolution of the vortical structures in a complex, unsteady flow.

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...

This paper presents a new 3D RGB image compression scheme designed for interactive real-time applications. In designing our compression method, we have compromised between two important goals: high compression ratio and fast random access ability, and have tried to minimize the overhead caused during runtime reconstruction. Our compression technique is suitable for applications wherein data are accessed in a somewhat unpredictable fashion, and real-time performance of decompression is necessary. The experimental results on three different kinds of 3D images from medical imaging, image-based rendering, and solid texture mapping suggest that the compression method can be used effectively in developing real-time applications that must handle large volume data, made of color samples taken in three- or higher-dimensional space.

Recent advances in realtime image compression and decompression hardware make it possible for a high-performance graphics engine to operate as a rendering server in a networked environment. If the client is a low-end workstation or set-top box, then the rendering task can be split across the two devices. In this paper, we explore one strategy for doing this. For each frame, the server generates a high-quality rendering and a low-quality rendering, subtracts the two, and sends the difference in compressed form. The client generates a matching low quality rendering, adds the decompressed difference image, and displays the composite. Within this paradigm, there is wide latitude to choose what constitutes a high-quality versus low-quality rendering. We have experimented with textured versus untextured surfaces, fine versus coarse tessellation of curved surfaces, Phong versus Gouraud interpolated shading, and antialiased versus nonantialiased edges. In all cases, our polygon-assisted compre...

Visualizing very large volume data has been recognized as a task requiring great effort in a variety of science and engineering fields. In particular, such data usually places considerable demands on run-time memory space. This paper describes an effective 3D compression scheme for very large volume data that exploits the power of wavelet theory. In designing our compression method, we have compromised between two important factors: high compression ratio and fast run-time random access. Our experimental results on the Visual Human data sets show that our method achieves fairly good compression ratios. In addition, it minimizes the overhead caused during run-time reconstruction of voxel values. This 3D compression scheme will be useful in developing many interactive visualization systems for huge volume data, and will make visualization technology accessible to a much wider range of users, as it can be based on personal computers or low-end workstations with limited memory. Keywords: ...

Interactive visualization of very large volume data has been recognized as a task requiring great effort in a variety of science and engineering fields. In particular, such data usually places considerable demands on run-time memory space. In this paper, we present an effective 3D compression scheme for interactive visualization of very large volume data, that exploits the power of wavelet theory. In designing our method, we have compromised between two important factors: high compression ratio and fast run-time random access ability. Our experimental results on the Visual Human data sets show that our method achieves fairly good compression ratios. In addition, it minimizes the overhead caused during run-time reconstruction of voxel values. This 3D compression scheme will be useful in developing many interactive visualization systems for huge volume data, especially when they are based on personal computers or workstations with limited memory. Keywords: very large volume data, wavele...

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...

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...

When interactive real-time applications are developed with very large volume data, the use of lossy compression is often inevitable. Lossy compression schemes generally encode data without consideration of the purpose of visualization that is actually performed, which often results in inefficient compression. In this paper, we present a new method for classifying voxels according to their importance in visualization, and assigning weights to them properly. The associated weight information can be combined with lossy compression schemes to reduce the visual degradation of reconstructed images, which, as a consequence, leads to higher compression rates and visual fidelity. Test results demonstrate that the proposed technique improves the quality of both compression and visualization significantly. 1