# Semantic Communication based on Large Language Model for Underwater Image Transmission

arXiv: https://arxiv.org/abs/2408.12616 · HTML: https://mlsanity.com/paper/2408.12616/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models](https://mlsanity.com/paper/2311.07575/) (2023)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021)
- [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018)
- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [Large AI Model-Based Semantic Communications](https://mlsanity.com/paper/2307.03492/) (2023)
- [Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis](https://mlsanity.com/paper/2209.12274/) (2022)
- [Semantic Communication Meets Edge Intelligence](https://mlsanity.com/paper/2202.06471/) (2022)
- [A Survey on Semantic Communications for Intelligent Wireless Networks](https://mlsanity.com/paper/2202.03705/) (2022)
- [Task-oriented Explainable Semantic Communications](https://mlsanity.com/paper/2302.13560/) (2023)
- [Large-Language-Model Enabled Semantic Communication Systems](https://mlsanity.com/paper/2407.14112/) (2024)
- [Language-Oriented Semantic Latent Representation for Image Transmission](https://mlsanity.com/paper/2405.09976/) (2024)
- [Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework](https://mlsanity.com/paper/2408.05112/) (2024)
- [Environment-Aware AUV Trajectory Design and Resource Management for Multi-Tier Underwater Computing](https://mlsanity.com/paper/2210.14619/) (2022)
- [A Unified Framework for Underwater Metaverse with Optical Perception](https://mlsanity.com/paper/2403.05567/) (2024)

## Built on by (most cited first, 6 of 6)

- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [Multimodal LLM Integrated Semantic Communications for 6G Immersive Experiences](https://mlsanity.com/paper/2507.04621/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [High Quality Underwater Image Compression with Adaptive Color Correction](https://mlsanity.com/paper/2505.09986/) (2025)
- [Exploring Textual Semantics Diversity for Image Transmission in Semantic Communication Systems using Visual Language Model](https://mlsanity.com/paper/2503.19386/) (2025)
- [Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments](https://mlsanity.com/paper/2509.23590/) (2025)

## Abstract (arXiv)

Underwater communication is essential for environmental monitoring, marine biology research, and underwater exploration. Traditional underwater communication faces limitations like low bandwidth, high latency, and susceptibility to noise, while semantic communication (SC) offers a promising solution by focusing on the exchange of semantics rather than symbols or bits. However, SC encounters challenges in underwater environments, including semantic information mismatch and difficulties in accurately identifying and transmitting critical information that aligns with the diverse requirements of underwater applications. To address these challenges, we propose a novel Semantic Communication (SC) framework based on Large Language Models (LLMs). Our framework leverages visual LLMs to perform semantic compression and prioritization of underwater image data according to the query from users. By identifying and encoding key semantic elements within the images, the system selectively transmits high-priority information while applying higher compression rates to less critical regions. On the receiver side, an LLM-based recovery mechanism, along with Global Vision ControlNet and Key Region ControlNet networks, aids in reconstructing the images, thereby enhancing communication efficiency and robustness. Our framework reduces the overall data size to 0.8% of the original. Experimental results demonstrate that our method significantly outperforms existing approaches, ensuring high-quality, semantically accurate image reconstruction.