Field Validation of Prior-Based Image Compression for Tetherless Operation of Underwater Remotely Operated Vehicles

Abstract

Efficient visual communication is critical for tetherless operation of underwater remotely operated vehicles, where acoustic links severely constrain bandwidth. Prior work introduced NVSPrior, which uses novel view synthesis with 3D Gaussian Splatting to encode scene priors, together with iNVS, a gradient-based refinement strategy for improving reconstruction quality. However, its performance degrades in real-world environments due to turbidity, lighting variability, and dynamic scene elements. This paper presents a systematic field evaluation of NVSPrior+iNVS in turbid natural waters using ROV trials off St. John’s Island, Singapore. To improve robustness, we introduce iNVS-w, which combines a DFNet-inspired pose regressor with a perceptual refinement loss. Benchmarking against classical and learned codecs shows that iNVS-w achieves substantially lower bitrate than scene-agnostic baselines while maintaining high perceptual fidelity on realistic field imagery. Ablation studies further quantify the role of initialization, loss functions, and feature extractors. These results provide a field-based assessment of prior-based image compression and identify practical modifications needed for robust operation in bandwidth-constrained underwater inspection.