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Abstract : |
Volume rendering is compute intensive and requires huge amounts of memory. Many methods were proposed to improve performance, but the problems of dealing with large data sets still remain. Muraki was the first to propose the decomposition of the volume density function into a pyramidal representation by means of the 3D discrete wavelet transform with orthonormal bases, and then render the corresponding basis functions weighted by the wavelet coefficients. Depending on the approximation properties of the basis functions used a large number of coefficients can be neglected while still achieving a good representation of the volume data, which results in very high compression rates. However, due to the pyramidal character of the discrete wavelet transform, a very large number of basis functions have to be evaluated in order to approximate the underlying volume density function at any point. This fact increases the rendering time drastically. Furthermore, the access rate to a data value is very slow for typical data structures for compressed data, which combined with an inefficient traversal of the pyramidal structures leads to even larger rendering times. In this paper we propose a data structure for sparse data representation, which does not exploit, |
