Scientific simulations and observations are producing vast amounts of time-varying vector field data, making it hard to store them for archival purposes and transmit them for analysis. Lossy compression is considered a promising approach to reducing these data because lossless compression yields low compression ratios that barely mitigate the problem. However, directly applying existing lossy compression methods to timevarying vector fields may introduce undesired distortions in critical-point trajectories, a crucial feature that encodes key properties of the vector field. In this work, we propose an efficient lossy compression framework that exactly preserves all critical-point trajectories in time-varying vector fields. Our contributions are threefold. First, we extend the theory for preserving critical points in space to preserving critical-point trajectories in space-time, and develop a compression framework to realize the functionality. Second, we propose a semi-Lagrange predictor to exploit the spatiotemporal correlations in advectiondominated regions, and combine it with the traditional Lorenzo predictor for improved compression efficiency. Third, we evaluate our method against state-of-the-art lossy and lossless compressors using four real-world scientific datasets. Experimental results demonstrate that the proposed method delivers up to 124.48X compression ratios while effectively preserving all critical-point trajectories. This compression ratio is up to 56.07X higher than that of the best lossless compressors, and none of the existing lossy compressors can preserve all critical-point trajectories at similar compression ratios.

翻译：科学模拟与观测正产生海量的时变矢量场数据，这给数据归档存储与分析传输带来了巨大挑战。有损压缩被视为减少此类数据量的有效途径，因为无损压缩的压缩比有限，难以根本解决问题。然而，将现有有损压缩方法直接应用于时变矢量场时，可能会在临界点轨迹（一种编码矢量场关键特性的核心特征）中引入非期望的失真。本研究提出了一种高效的有损压缩框架，能够精确保持时变矢量场中所有临界点的轨迹。我们的贡献主要体现在三个方面：首先，我们将空间临界点保持理论扩展至时空临界点轨迹保持，并构建了实现该功能的压缩框架；其次，我们提出一种半拉格朗日预测器以利用平流主导区域的时空相关性，并将其与传统洛伦佐预测器结合以提升压缩效率；最后，我们使用四个真实科学数据集，将所提方法与当前先进的有损及无损压缩器进行对比评估。实验结果表明，该方法在有效保持所有临界点轨迹的同时，最高可实现124.48倍的压缩比。这一压缩比较最佳无损压缩器提升达56.07倍，且现有有损压缩器在相近压缩比下均无法完全保持所有临界点轨迹。