Efficient JPEG-AI Image Coding for Remote Sensing Semantic Segmentation

Efficient image compression is crucial for remote sensing satellite systems, as it determines the performance of machine vision applications analyzing the downlinked image data at ground stations. However, existing conventional or learning-based image compression approaches exhibit limitations in either high complexity or undesirable vision task performance. This letter investigates an efficient neural image compression standard, JPEG-AI-based self-supervised remote sensing image compression approach, SS-JPEG-AI, for semantic segmentation tasks. Our approach maintains the low-complexity advantages of JPEG-AI while incorporating (1) a computationally efficient transformer-based attention mechanism that enhances reconstruction quality without increasing encoder complexity for onboard systems and (2) a contrastive learning strategy that improves feature discriminability and sharpens inter-category decision boundaries for segmentation tasks. Compared to the state-of-the-art image compression methods, SS-JPEG-AI achieves better Bjøntegaard Delta-rate (BD-rate) performance across the mean Intersection over Union (mIoU) and mean F-score (mFscore) while maintaining up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30 times</i> smaller computational complexity.