Implications of Neural Compression to Scientific Images

Abstract

While neural compression has the potential to revolutionize image compression, recent studies have emphasized its ability to introduce subtle artifacts that could alter the image content. Concerned about the impact of such modifications on scientific images, this work explores the potential effects of neural compression on these images, focusing on two critical aspects: semantic understanding and forensic integrity. We use scientific image datasets to assess the performance of neural compression techniques on Visual Question Answering (VQA) and copy-move forgery detection tasks. Our findings indicate that the subtle changes introduced by neural compression do not significantly degrade the performance of state-of-the-art solutions. In the experiments, neurally compressed images sufficiently preserved the original semantics and forensic traces. Moreover, compared to lossy techniques, e.g., JPEG compression, at similar bit-per-pixel (bpp) rates, neural compression demonstrates a superior ability to preserve both semantic content and forensic traces, even at high compression levels. Our results suggest that neural compression may provide a viable alternative to lossy compression for scientific images.