Best Paper Award
Cells (left), Fiber (middle), and Cortex (right) data sets rendered interactively as compressed segmentation volumes.
Our lossless method achieves strong compression ratios and provides fast decompression and adaptive level-of-detail.
Data set sources are cited in the paper.
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.