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.

翻译：基于体素的分割体积通常存储大量标签和体素，由此产生的数据量可能导致存储、传输和交互式可视化变得困难。我们提出一种无损压缩技术来解决这些挑战。该技术处理分割体积的单个小块，通过迭代细化方案紧凑地编码标记区域及其边界。每个小块的结果包括一个标签列表，以及用于重建该小块的序列操作，该序列进一步使用rANS熵编码进行压缩。由于不同小块间操作的相对频率高度相似，熵编码可为整个数据集使用全局频率表，从而支持高效并行的（解）压缩。我们的技术实现了高吞吐量（压缩和解压缩均可达到每秒千兆字节）和强压缩比（约为原始数据集大小的1%至3%），同时适用于基于GPU的渲染。我们针对来自不同领域的多种数据集评估了该方法，并展示了基于GPU的体素可视化，支持即时解压缩、细节层次渲染（可选择性将细节系数按需流式传输至GPU），以及针对解压缩块采用缓存策略以进一步提升性能。