Summary of Efficient Semantic Communication Through Transformer-aided Compression, by Matin Mortaheb and Mohammad A. Amir Khojastepour and Sennur Ulukus
Efficient Semantic Communication Through Transformer-Aided Compression
by Matin Mortaheb, Mohammad A. Amir Khojastepour, Sennur Ulukus
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Signal Processing (eess.SP)
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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 A novel channel-aware adaptive framework for semantic communication is introduced, leveraging vision transformers to dynamically compress images based on their semantic content. This approach interprets the attention mask as a measure of patch relevance and adapts encoding resolution to channel bandwidth, ensuring critical information preservation even in limited environments. The proposed method enhances communication efficiency by optimizing bandwidth, demonstrated through evaluation using the TinyImageNet dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to send images is developed, which helps improve how data is transmitted over wireless networks. This approach uses special computers called transformers to understand what’s important in an image and compress it accordingly. It works like a filter, making sure that only the most important parts of the image are sent, even if there’s not much bandwidth available. The results show that this new method can accurately send images while using less bandwidth than before. |
Keywords
* Artificial intelligence * Attention * Mask
