  Voxel-based segmentation volumes often store a large number of labels and
voxels, and the resulting amount of data can make storage, transfer, and
interactive visualization difficult. We present a lossless compression
technique which addresses these challenges. It processes individual small
bricks of a segmentation volume and compactly encodes the labelled regions and
their boundaries by an iterative refinement scheme. The result for each brick
is a list of labels, and a sequence of operations to reconstruct the brick
which is further compressed using rANS-entropy coding. As the relative
frequencies of operations are very similar across bricks, the entropy coding
can use global frequency tables for an entire data set which enables efficient
and effective parallel (de)compression. Our technique achieves high throughput
(up to gigabytes per second both for compression and decompression) and strong
compression ratios of about 1% to 3% of the original data set size while being
applicable to GPU-based rendering. We evaluate our method for various data sets
from different fields and demonstrate GPU-based volume visualization with
on-the-fly decompression, level-of-detail rendering (with optional on-demand
streaming of detail coefficients to the GPU), and a caching strategy for
decompressed bricks for further performance improvement.

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