In high-performance computing (HPC) environments, particularly in synchrotron radiation facilities, vast amounts of X-ray images are generated. Processing large-scale X-ray Computed Tomography (X-CT) datasets presents significant computational and storage challenges due to their high dimensionality and data volume. Traditional approaches often require extensive storage capacity and high transmission bandwidth, limiting real-time processing capabilities and workflow efficiency. To address these constraints, we introduce a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features. Our work reduces data volume while preserving critical information for downstream processing tasks. At pre-processing stage, we utilize error-bounded quantization to reduce the amount of data to be processed and therefore improve computational efficiencies. At the compression stage, our methodology combines object extraction with multiple state-of-the-art lossless and lossy compressors, resulting in significantly improved compression ratios. We evaluated this framework against seven X-CT datasets and observed a relative compression ratio improvement of 12.34x compared to the standard compression.

翻译：在高性能计算（HPC）环境中，尤其是在同步辐射设施中，会产生海量的X射线图像。大规模X射线计算机断层扫描（X-CT）数据集因其高维度和大数据量，在处理时面临显著的计算与存储挑战。传统方法通常需要巨大的存储容量和高传输带宽，这限制了实时处理能力和工作流效率。为应对这些限制，我们提出了一种基于感兴趣区域（ROI）驱动的提取框架（ROIX-Comp），该框架通过识别并仅保留关键特征，智能地压缩X-CT数据。我们的工作在减少数据量的同时，为下游处理任务保留了关键信息。在预处理阶段，我们采用误差有界量化来减少待处理的数据量，从而提升计算效率。在压缩阶段，我们的方法将对象提取与多种先进的无损及有损压缩器相结合，实现了显著提升的压缩比。我们在七个X-CT数据集上对该框架进行了评估，观察到相较于标准压缩方法，其相对压缩比提升了12.34倍。