New algorithm aids image compression of data sets
Researchers in Slovenia have proposed a new algorithm for the lossless compression of volumetric data sets, a finding that may allow CT and MR modalities to compress voxel data during acquisition. The new method builds a quadtree representation of each slice (Comput Med Imaging Graph 2005;29(4):305-312).
Researchers in Slovenia have proposed a new algorithm for the lossless compression of volumetric data sets, a finding that may allow CT and MR modalities to compress voxel data during acquisition. The new method builds a quadtree representation of each slice (Comput Med Imaging Graph 2005;29(4):305-312).
Most existing lossy algorithms use octree encoding for compression, which does not exploit the coherence between neighboring slices but instead creates a common voxel space. This requires large onboard memory, according to researchers at the University of Maribor.
The researchers instead use a different compression approach, processing two consecutive slices at a time. Some properties of voxel data sets, such as homogeneity and coherence, can be used more efficiently to produce better compression ratios.
The quadtree method is similar to the interframe-based digital video compression method used by MPEG-2, in that it exploits similarities between neighboring slices. In contrast to MPEG-2, which forms a set of equally sized blocks of pixels (macroblocks) and predicts where they would move in the next frame, the group's algorithm uses macroblocks of different sizes and does not make any prediction. The positions and sizes of the macroblocks are coded by a quadtree.
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