Summary of Semantic Communication Based on Large Language Model For Underwater Image Transmission, by Weilong Chen et al.
Semantic Communication based on Large Language Model for Underwater Image Transmission
by Weilong Chen, Wenxuan Xu, Haoran Chen, Xinran Zhang, Zhijin Qin, Yanru Zhang, Zhu Han
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel Semantic Communication (SC) framework for underwater communication, which leverages Large Language Models (LLMs) to perform semantic compression and prioritization of underwater image data. The framework identifies key semantic elements within images and selectively transmits high-priority information while applying higher compression rates to less critical regions. On the receiver side, an LLM-based recovery mechanism aids in reconstructing images, enhancing communication efficiency and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re exploring the ocean floor, collecting data on marine life. Traditional underwater communication methods are slow and prone to errors. This new approach uses AI-powered “understanding” of images to compress and prioritize important information, making it faster and more reliable. The system can even identify and reconstruct critical details like sea creatures or habitats. |
