Residual Modeling for High-Fidelity Learned Compression of Scientific Data

This paper proposes a novel residual-centric approach for high-fidelity learned compression of scientific data. It introduces innovative residual coders designed to overcome limitations of existing methods, improving accuracy in high-tolerance regimes.