Information-theoretic exploration for texture-based visualization

In this paper, we present a novel texture-based visualizing algorithm. This algorithm can be used as a robust and effective descriptor based on view-dependent information-theoretic statistical analysis of flow data. We calculate local entropy values to measure the distinct feature structures in the underlying field. Volume rendering is used to visualize the extracted flow patterns automatically. Due to the high computational expense, texture construction and rendering skip all empty and unimportant blocks. And an efficient GPU implementation is integrated together for high interactive frame rates. We show the key components of our approach with detailed analysis, and demonstrate that our method can visualize 2D and 3D flow data effectively.Graphical abstract

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