Identifying Outliers in AI-based Image Compression

Image compression using artificial intelligence (AI) is gaining importance in scientific research, where instruments and simulations can produce hundreds of images per second. Effective compression with high ratios is essential for facilitating discoveries. A key challenge is the automatic detection of outliers—cases where compression fails or significant phenomena are present. To address this, we developed a consensus-driven methodology using unsupervised machine learning techniques for identifying outlier compressed images. We evaluated our approach on unlabeled datasets, including microscopy and X-ray images, successfully identifying multiple outliers using metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), structural texture similarity index measure (STSIM) and deep image and structural texture similarity index (DISTS).