Volume Visualization has emerged as one of the most active fields in Computer Graphics because of its wide range of applications in various disciplines: earth sciences, medicine and biology, Rendering volume data that evolve along (time time-varying datasets) is one of the major challenges of visualization.
This thesis addresses direct volume rendering of time-varying datasets. Its goal is to contribute to the enhancement of the efficiency of the visualization of time- varying datasets by providing interactive exploration of the data through time with moving camera and changing transfer functions. We focus on using compression and optimized data structures and algorithms to manage data efficiently. Specifically, the thesis contribution is the acceleration of time-varying volume rendering using Temporal Run-Length Encoding (TRL) of data. We propose and compare the use of TRL in three different volume rendering strategies: ray-casting, 3D texture-mapping and splatting.
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