| In this thesis, we address the problem of large-scale data visualization from two aspects, dimensionality and resolution. We introduce a novel data structure called Differential Time-Histogram Table (DTHT) for visualization of time-varying (4D) scalar data. The proposed data structure takes advantage of the coherence in time-varying datasets and allows efficient updates of data necessary for rendering during data exploration and visualization while guaranteeing that the scalar field visualized is within a given error tolerance of the scalar field sampled.;To address the high-resolution datasets, we propose a hierarchical data structure and introduce a novel hybrid framework to improve the quality of multi-resolution visualization. For more accurate rendering at coarser levels of detail, we reduce aliasing artifacts by approximating data distribution with a Gaussian basis at each level of detail and we reduce blurring by using transparent isosurfaces to capture high-frequency features usually missed in coarse resolution renderings. |
