Fast Compressed Segmentation Volumes for Scientific Visualization

TL;DR

This paper introduces a lossless, high-throughput compression method for voxel segmentation volumes that enables efficient storage, transfer, and real-time GPU visualization by encoding small volume bricks with an iterative scheme and entropy coding.

Contribution

It presents a novel brick-based lossless compression technique with parallel decompression suitable for GPU visualization, achieving high speed and strong compression ratios.

Findings

Compression ratios of 1% to 3% of original size.

Achieves up to gigabytes per second in compression and decompression.

Enables real-time GPU-based volume visualization with on-the-fly decompression.

Abstract

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.