Codebook-Empowered Analysis-Friendly Extreme Underwater Image Compression

While existing underwater image compression (UIC) methods optimize for human perception or basic redundancies, they neglect inter-image correlations and fail to prioritize machine-friendly features essential for automated analysis. This paper introduces a novel -quantized (VQ) codebook-driven framework for machine-centric UIC. We leverage VQ codebooks -- pre-trained as external priors on diverse underwater data -- to unify three critical stages: (1) Machine-friendly feature extraction via contrastive learning with high/low-quality codebooks, enhancing degradation robustness; (2) Compact compression using variable-size codebooks to map discriminative features to entropy-coded indices, enabling ultra-low bitrates ($