Random Access Segmentation Volume Compression for Interactive Volume Rendering
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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 2× to 4× smaller data sets at rates between 0.648% and 4.411% of the original volume sizes.
Description
CCS Concepts: Computing methodologies → Rendering; Information systems → Data compression
@article{10.1111:cgf.70116,
journal = {Computer Graphics Forum},
title = {{Random Access Segmentation Volume Compression for Interactive Volume Rendering}},
author = {Piochowiak, Max and Kurpicz, Florian and Dachsbacher, Carsten},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70116}
}