In this paper, we address the problem of the efficient visualization of large irregular volume data sets by exploiting a multiresolution model based on tetrahedral meshes.

We present a new framework for managing and rendering large scale time-varying data using the wavelet-based time-space partitioning (WTSP) tree. We utilize the hierarchical TSP tree data structure to capture both spatial and temporal locality and coherence of the underlying time-varying data and exploit the wavelet transform to convert the data into a multiresolution spatio-temporal representation. Coupled with the construction of TSP tree, a twostage wavelet transform process (3D+1D) is applied to the data in the spatial and temporal domains respectively at the preprocessing step. During rendering, the wavelet-compressed data stream is decompressed on-the-fly and rendered using 3D hardware texture mapping. WTSP tree allows random access of data at arbitrary spatial and temporal resolutions at run time. The user is provided with flexible error control of image quality and rendering speed tradeoff. We demonstrate the effectiveness and utility of our framework by rendering gigabytes of time-varying data sets on a single off-theshelf PC.

We present a new parallel multiresolution volume rendering framework for large-scale time-varying data visualization using the wavelet-based time-space partitioning (WTSP) tree. Utilizing the wavelet transform, a largescale time-varying data set is converted into a space-time multiresolution data hierarchy, and is stored in a timespace partitioning (TSP) tree. To eliminate the parent-child data dependency for reconstruction and achieve loadbalanced rendering, we design an algorithm to partition the WTSP tree and distribute the wavelet-compressed data along hierarchical space-filling curves with error-guided bucketization. At run time, the WTSP tree is traversed according to the user-specified time step and tolerances of both spatial and temporal errors. Data blocks of different spatio-temporal resolutions are reconstructed and rendered to compose the final image in parallel. We demonstrate that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.

Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.

Bloom filtering is an important technique for space efficient storage of a conservative approximation of a set S. The set stored may have up to some specified number of false positive members, but all elements of S are included. In this paper we consider lossy dictionaries that are also allowed to have false negatives, i.e., leave out elements of S. The aim is to maximize the weight of included keys within a given space constraint. This relaxation allows a very fast and simple data structure making almost optimal use of memory. Being more time efficient than Bloom filters, we believe our data structure to be well suited for replacing Bloom filters in some applications. Also, the fact that our data structure supports information associated to keys paves the way for new uses, as illustrated by an application in lossy image compression.

We present several improvements for compression based multi-resolution rendering of very large volume data sets at interactive to real-time frame rates on standard PC hardware. The algorithm accepts scalar or multi-variant data sampled on a regular grid as input. The input data is converted into a compressed hierarchical wavelet representation in a pre-processing step. During rendering, the wavelet representation is decompressed on-the-fly and rendered using hardware texture mapping. The level-of-detail used for rendering is adapted to the estimated screen-space error. To increase the rendering performance additional visibility tests, such as empty space skipping and occlusion culling, are applied. Furthermore we discuss how to render the remaining multi-resolution blocks efficiently using modern graphics hardware. Using a prototype implementation of this algorithm we are able to perform a high quality interactive rendering of large data sets on a single off-the-shelf PC.

Abstract: With development of biomedical and networking technology, interactive volume rendering with data transmitted via the network becomes an interesting topic. Two technical problems are data transmission and fast volume rendering. In this paper, two schemes using Wavelet foveation are proposed and implemented: the region-based and coarse-to-fine. The first scheme transmits and renders the region of interest (ROI) of volume data on the client site at highest resolution for the first time, and consequently it iteratively expands the region layer by layer towards to the peripheral. While the second one gives a complete image in a low resolution for the first time on the client. The detail coefficients will be transmitted continuously and the rendering result will be progressively refined depending on the distance to the ROI. 1

A determinacao de pontos de intersecao e contatos entre fronteiras de objetos nao e uma tarefa simples. Uma forma de facilitar essa operacao usa-se estruturas hierarquicas para issolar rapidamente os pedacos de segmentos em possvel intersecao, para finalmente se computar localmente os pontos de contato. Neste trabalho formula-se um novo metodo de representacao em estrutura hierarquica de caixas orientadas envolventes adaptado e ajustadamente de segmentos de fronteiras de objetos definidos por curvas B-splines cubicas fechadas com perturbacoes. Cada par de caixas adjacentes de um nvel define uma outra caixa orientada. Cada caixa e computada de forma adatada e ajustada aos segmentos de fronteira do objeto usando-se indicadores estatsticos de segunda ordem. O resultado computacional, em anima coes de objetos rgidos de geometria complexa em computacao grafica, mostra que o metodo formulado e eficiente comparado com outros metodos existentes.

Great advancements in commodity graphics hardware have favored GPU-based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms. Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time-varying or multi-volume visualization, or for networked visualization on the emerging mobile devices. To address this issue, a variety of level-of-detail data representations and compression techniques have been introduced. In order to improve capabilities and performance over the entire storage, distribution and rendering pipeline, the encoding/decoding process is typically highly asymmetric, and systems should ideally compress at data production time and decompress on demand at rendering time. Compression and level-of-detail pre-computation does not have to adhere to real-time constraints and can be performed off-line for high quality results. In contrast, adaptive real-time rendering from compressed representations requires fast, transient, and spatially independent decompression. In this report, we review the existing compressed GPU volume rendering approaches, covering compact representation models, compression techniques, GPU rendering architectures and fast decoding techniques.