Abstract
Traditional compression methods struggle to preserve critical mesoscale ocean features like vortices during bandwidth-constrained marine data transmission. To aaddress this limitation, we propose CompressGAN, a novel deep learning framework that transcends conventional approaches reliant on generic image metrics (e.g., peak signal-to-noise ratio, PSNR; structural similarity index, SSIM). The architecture integrates global-local dual discriminators to enforce spatiotemporal coherence of mesoscale vortices, employs dilated convolutions to enhance feature receptive fields without computational overhead, and incorporates vortex recognition rate as a physics-aware evaluation metric. Furthermore, parametric pruning and adaptive quantization strategies are embedded to optimize memory efficiency for shipborne hardware constraints. Validation across multiple ocean reanalysis datasets demonstrates CompressGAN's superiority at 4 × compression ratios, achieving 91.46% mesoscale eddy identification accuracy (Iden) versus SRGAN (89.71%) and SRResNet (89.82%), while maintaining operational efficiency (148 s/image inference time, 25 GB peak memory). Generalization tests reveal controlled performance degradation: PSNR reduced by 4.2 ± 0.3 dB, SSIM by 0.7126, and Iden by 4.1%, confirming robustness under marine operational scenarios. This work resolves the critical trade-off between vessel-mounted computational limits and real-time ocean data demands, providing a viable pathway for integrated shipboard systems to reconcile multimodal marine data processing with navigation service requirements.