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：科学模拟与观测产生了海量的时变向量场数据，这给数据的归档存储与分析传输带来了巨大挑战。由于无损压缩的压缩比过低，难以有效缓解该问题，有损压缩被视为减少此类数据量的有效途径。然而，将现有的有损压缩方法直接应用于时变向量场时，可能会在临界点轨迹——这一编码向量场关键特性的重要特征——中引入不期望的失真。本文提出了一种高效的有损压缩框架，能够精确地保持时变向量场中所有的临界点轨迹。我们的贡献主要体现在三个方面：首先，我们将保持空间中临界点的理论扩展至保持时空中的临界点轨迹，并开发了一个压缩框架来实现该功能。其次，我们提出了一种半拉格朗日预测器，用于利用平流主导区域中的时空相关性，并将其与传统的洛伦佐预测器相结合，以提高压缩效率。第三，我们使用四个真实世界的科学数据集，将所提方法与最先进的有损和无损压缩器进行了对比评估。实验结果表明，所提方法在有效保持所有临界点轨迹的同时，能够实现高达124.48倍的压缩比。这一压缩比相比最佳无损压缩器最高提升了56.07倍，并且现有有损压缩器在相近压缩比下均无法保持所有临界点轨迹。