In this work, we address the problem of lossless compression of scientific and medical floating-point volume data. We propose two prediction-based compression methods that share a common framework, which consists of a switched prediction scheme wherein the best predictor out of a preset group of linear predictors is selected. Such a scheme is able to adapt to different datasets as well as to varying statistics within the data. The first method, called APE (Adaptive Polynomial Encoder), uses a family of structured interpolating polynomials for prediction, while the second method, which we refer to as ACE (Adaptive Combined Encoder), combines predictors from previous work with the polynomial predictors to yield a more flexible, powerful encoder that is able to effectively decorrelate a wide range of data. In addition, in order to facilitate efficient visualization of compressed data, our scheme provides an option to partition floating-point values in such a way as to provide a progressive representation. We compare our two compressors to existing state-of-the-art lossless floating-point compressors for scientific data, with our data suite including both computer simulations and observational measurements. The results demonstrate that our polynomial predictor, APE, is comparable to previous approaches in terms of speed but achieves better compression rates on average. ACE, our combined predictor, while somewhat slower, is able to achieve the best compression rate on all datasets, with significantly better rates on most of the datasets.

In order to assess the reliability of volume rendering, it is necessary to consider the uncertainty associated with the volume data and how it is propagated through the volume rendering algorithm, as well as the contribution to uncertainty from the rendering algorithm itself. In this work, we show how to apply concepts from the field of reliable computing in order to build a framework for management of uncertainty in volume rendering, with the result being a self-validating computational model to compute a posteriori uncertainty bounds. We begin by adopting a coherent, unifying possibility-based representation of uncertainty that is able to capture the various forms of uncertainty that appear in visualization, including variability, imprecision, and fuzziness. Next, we extend the concept of the fuzzy transform in order to derive rules for accumulation and propagation of uncertainty. This representation and propagation of uncertainty together constitute an automated framework for management of uncertainty in visualization, which we then apply to volume rendering. The result, which we call fuzzy volume rendering, is an uncertainty-aware rendering algorithm able to produce more complete depictions of the volume data, thereby allowing more reliable conclusions and informed decisions. Finally, we compare approaches for self-validated computation in volume rendering, demonstrating that our chosen method has the ability to handle complex uncertainty while maintaining efficiency.

Hardware-accelerated volume rendering using the GPU is now the standard approach for real-time volume rendering, although limited graphics memory can present a problem when rendering large volume data sets. Volumetric compression in which the decompression is coupled to rendering has been shown to be an effective solution to this problem; however, most existing techniques were developed in the context of software volume rendering, and all but the simplest approaches are prohibitive in a real-time hardware-accelerated volume rendering context. In this paper we present a novel block-based transform coding scheme designed specifically with real-time volume rendering in mind, such that the decompression is fast without sacrificing compression quality. This is made possible by consolidating the inverse transform with dequantization in such a way as to allow most of the reprojection to be precomputed. Furthermore, we take advantage of the freedom afforded by off-line compression in order to optimize the encoding as much as possible while hiding this complexity from the decoder. In this context we develop a new block classification scheme which allows us to preserve perceptually important features in the compression. The result of this work is an asymmetric transform coding scheme that allows very large volumes to be compressed and then decompressed in real-time while rendering on the GPU.

An important challenge in the application of direct volume rendering to time-varying data is the specification of transfer functions for all time steps. Very little research has been devoted to this problem, however. To address this issue we propose an approach which allows simultaneous classification of the entire time series. We explore options for transfer function specification that are based, either directly or indirectly, on the time histogram. Furthermore, we consider how to effectively provide feedback for interactive classification by exploring options for simultaneous rendering of the time series, again based on the time histogram. Finally, we apply this approach to several large time-varying data sets where we show that the important features at all times are captured with about the same effort it takes to classify one time step using conventional classification.

Rendering directly from packed or compressed volume data formats using graphics hardware has advantages in terms of memory consumption and bandwidth, but results in lower-quality images due to the prohibitive cost of performing interpolation and gradient-based shading on the reconstructed data. The problem with the existing method lies in its close coupling of decompression and interpolation. We demonstrate that deferred filtering overcomes this problem by using a two-pass decompression and rendering strategy. With this method interpolation and gradient calculations are very efficient, allowing high quality rendering directly from packed or compressed volume data. We evaluate the cost of creating interpolated, gradient-shaded renderings using traditional on-the-fly decompression and deferred filtering, and show that deferred filtering can provide up to twenty times speed-up for high quality rendering.