Sketch-based Extreme Underwater Image Compression Network

Yu Zhang, Liquan Shen, Mengyao Li, Meng Yu, Shiwei Wang, Feifeng Wang

Abstract: Underwater applications such as exploration and salvage operations require capturing underwater images (UWIs) to evaluate attributes such as the shape and structural integrity of submerged targets. However, underwater image transmission faces significant challenges due to the limited wireless acoustic channel available in underwater communication systems. Existing image compression algorithms struggle with limited compression ratios, which leads to a loss of crucial structural information and poor reconstruction quality, making them unsuitable for underwater practical applications. To overcome these limitations, we propose a sparse Sketch-based Extreme Underwater Compression framework (SEUCN), which mainly includes two sub-networks: Sparse Sketch Generation Network (SSGN) and Underwater Prior-guided Reconstruction Network (UPRN). To reduce redundancy and ensure effective compression at extremely low bitrates, the SSGN is designed to generate a compression-friendly sparse structural sketch through two ways. Firstly, it focuses on extracting important structural information to support analysis tasks within the constraints of limited bitrates. Secondly, it incorporates an underwater imaging model to focus on learning critical texture information for visual reconstruction. To restore the information lost during compression and achieve high-quality reconstruction, UPRN is designed to enhance structure details, restore underwater style, and enrich texture information during the reconstruction of UWIs from the decoded sketches, by effectively integrating multiple sources of prior knowledge. Specially, considering the high similarity of semantics and texture across different UWIs with common targets, the Dictionary-guided Texture Recovery Module (DTRM) leverages a universal underwater multi-scale feature dictionary as texture prior knowledge to supplement missing texture details. Extensive experiments show that our SEUCN demonstrates outstanding performance in retaining significant structural information to assist underwater practical tasks, and achieves superior visual quality compared to existing methods.

External IDs:doi:10.1109/tcsvt.2025.3634125