Lossless Compression of Modern Astronomical Data Using a Novel Learned Predictor

The rapid growth of science-ready data volumes generated by modern ground-based astronomical observatories poses significant challenges for data storage and transmission. The de facto standard for storing these data is FITS, which incorporates a limited set of built-in compression methods. In this work, we present a novel lossless compression approach for astronomical images based on a weighted linear predictor combined with a CCSDS 123.0-B-2 mapper and a contextual binary arithmetic encoder. Three predictor configurations are evaluated, ranging from handcrafted designs to an optimized, dataset-adaptive model. Experimental results show consistent improvements over state-of-the-art lossless compressors. The optimized predictor achieves the best performance, improving compression by up to 0.471 bits per pixel (6.40%) compared to the best-performing FITS compressor, FPACK Hcompress. When applied to large-scale instruments such as the VLT, this corresponds to a reduction of approximately 0.25 TB per day (4.48%), yielding annual storage savings exceeding 90 TB. The implementation is publicly available at https://github.com/xavifeme00/Astronomy-Compressor.