Quantifying the Impact of XCT Data Compression on Defect Shapes

X-ray computed tomography represents an essential tool in non-destructive analysis of industrial components in a wide variety of applications, ranging from fast inline inspection, to imaging of very large structures such as cars, and micro-scale characterization of novel material candidates. Due to the high demand regarding detailed analyses, both hardware and software components are improving continuously, yielding larger quantities of three-dimensional datasets. Data compression techniques aim at reducing storage requirements, but yet there is a risk of losing valuable information if a compression is too strong. In this work, we thus investigate a three-dimensional lossy compression technique tailor-made for XCT datasets and evaluate the deterioration of defect representations due to compression. For this purpose, a virtual XCT dataset is generated by modeling defects of randomized shapes and using X-ray simulation to generate realistic XCT data, whereas the pipeline will be applied on real XCT data as well in the full paper. While varying the maximum admissible compression error, an image processing pipeline will segment the defects and evaluate a selection of shape factors. When increasing the compression factor, the quantitative results indicate a continuous drop in shape factors, i.e., a moderate loss in shape is encountered at high compression rates.