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Volume visualization with random data access poses significant challenges. While tiling techniques lead to simple implementations, they are not well suited for cases where the goal is to access arbitrarily located subdimensional datasets (e.g., displaying a 'band' of several parallel lines of arbitrary orientation from a 2D image; being able to display an arbitrary 2D planar 'cut' from a 3D volume). Significant effort has been devoted to volumetric data compression, with most techniques proposing to tile volumes into cuboid subvolumes to enable random access. In this paper we show that, in cases where subdimensional
datasets are accessed, this leads to significant transmission inefficiency. As an alternative, we propose novel
server-client based data representation and retrieval methods which can be used for fast random access of low dimensional data from high dimensional datasets. In this paper, 2D experiments are shown but the approach can be extended to higher dimensional datasets. We use multiple redundant tilings of the image, where each tiling has a different orientation. We discuss the 2D rectangular tiling scheme and two main algorithm components of such 2D system, namely, (i) a fast optimal search algorithm to determine which tiles should be retrieved for a given query and (ii) a mapping algorithm to enable
efficient encoding without interpolation of rotated tiles. In exchange for increased server storage, we demonstrate that significant reductions, a factor of 2 in bandwidth reduction, can be achieved relative to conventional square tiling techniques. The transmission rate can be reduced even more by allowing more storage overhead. This method speeds up the random access procedure and saves memory on the user's side. Here we use the 2D example to retrieve random lines (or sets of lines) from a 2D image. While our experiments are based on extracting 1D data from 2D datasets, the proposed method can be extended to 3D or higher dimensions. The associated basic concepts and analysis (namely the extraction of 2D slices from 3D datasets) and a more detailed discussion focusing on the 3D and higher dimensional case will be presented in
another paper. In this paper, we design a tiling method that locates the rotation centers at points on a square Cartesian grid pattern and has the tile rotation angles uniformly distributed around each rotation center. The angles of the tiles associated to each rotation center are the same. Other various ways of tiling method design are also possible The performance by using other tiling methods will be addressed in future work.
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