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，数据量过大会给存储、传输和交互式可视化带来挑战。我们提出一种无损压缩技术来解决这些问题。该方法通过迭代细化方案，逐一处理分割体积中的小型块，紧凑地编码标记区域及其边界。每个块的处理结果包含一个标签列表，以及一系列用于重建该块的操作序列，并通过rANS熵编码进一步压缩。由于各块中的操作相对频率高度相似，熵编码可使用全局频率表处理整个数据集，从而实现高效并行的（解）压缩。我们的技术可实现高吞吐量（压缩和解压缩速度可达吉字节/秒），压缩比约为原始数据大小的1%至3%，且适用于基于GPU的渲染。我们使用不同领域的多种数据集评估该方法，展示了基于GPU的体素可视化技术，包括实时解压缩、细节层次渲染（支持按需向GPU流式传输细节系数）以及解压缩块的缓存策略以进一步提升性能。