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 vector-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 (less than 0.04bpp); and

(3) Feature refinement at the decoder, restoring semantic fidelity for downstream tasks. In addition, we contribute the first Underwater Visual Question Answering (UVQA) benchmark to holistically evaluate machine perception across object presence, counting, and localization. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art codecs in machine vision task performance at ultra-low bitrates. The VQ-codebook effectively harnesses inter-image redundancy, combats joint degradation, and delivers compact, analysis-friendly representations, establishing a new paradigm for machine-centric UIC.