• Electrical Engineering and Systems Science > Image and Video Processing [Submitted on 17 Feb 2026] Title:ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction View PDF HTML (experimental)Abstract: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.
Article Summaries:
- Researchers have introduced ROIX‑Comp, a region‑of‑interest (ROI)-driven framework designed to reduce the size of X‑ray computed tomography (X‑CT) data in high‑performance computing environments such as synchrotron facilities. The method first applies error‑bounded quantization during pre‑processing to limit data volume, then extracts key features and compresses them using a mix of state‑of‑the‑art lossless and lossy algorithms. Tested on seven large X‑CT datasets, ROIX‑Comp achieved an average compression ratio improvement of 12.34× over conventional compression techniques, thereby easing storage demands and enabling more efficient real‑time processing.
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