Abstract
Segmentation volumes are voxel data sets often used in machine learning, connectomics, and natural sciences. Their large sizes
make compression indispensable for storage and processing, including GPU video memory constrained real-time visualization.
Fast Compressed Segmentation Volumes (CSGV) [PD24] provide strong brick-wise compression and random access at the brick
level. Voxels within a brick, however, have to be decoded serially and thus rendering requires caching of visible full bricks,
consuming extra memory. Without caching, accessing voxels can have a worst-case decoding overhead of up to a full brick
(typically over 32.000 voxels). We present CSGV-R which provide true multi-resolution random access on a per-voxel level. We
leverage Huffman-shaped Wavelet Trees for random accesses to variable bit-length encoding and their rank operation to query
label palette offsets in bricks. Our real-time segmentation volume visualization removes decoding artifacts from CSGV and renders
CSGV-R volumes without caching bricks at faster render times. CSGV-R has slightly lower compression rates than CSGV, but
outperforms Neuroglancer, the state-of-the-art compression technique with true random access, with 2x to 4x smaller data sets
at rates between 0.648% and 4.411% of the original volume sizes.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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