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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01053 |
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| _version_ | 1866914421885894656 |
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| author | Li, Chongyang He, Yanmei Zhang, Tianqian He, Mingjian Liu, Shouyin |
| author_facet | Li, Chongyang He, Yanmei Zhang, Tianqian He, Mingjian Liu, Shouyin |
| contents | This paper proposes a novel knowledge-Base (KB) assisted semantic communication framework for image transmission. At the receiver, a Facebook AI Similarity Search (FAISS) based vector database is constructed by extracting semantic embeddings from images using the Contrastive Language-Image Pre-Training (CLIP) model. During transmission, the transmitter first extracts a 512-dimensional semantic feature using the CLIP model, then compresses it with a lightweight neural network for transmission. After receiving the signal, the receiver reconstructs the feature back to 512 dimensions and performs similarity matching from the KB to retrieve the most semantically similar image. Semantic transmission success is determined by category consistency between the transmitted and retrieved images, rather than traditional metrics like Peak Signal-to-Noise Ratio (PSNR). The proposed system prioritizes semantic accuracy, offering a new evaluation paradigm for semantic-aware communication systems. Experimental validation on CIFAR100 demonstrates the effectiveness of the framework in achieving semantic image transmission. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge-Base based Semantic Image Transmission Using CLIP Li, Chongyang He, Yanmei Zhang, Tianqian He, Mingjian Liu, Shouyin Computer Vision and Pattern Recognition Artificial Intelligence This paper proposes a novel knowledge-Base (KB) assisted semantic communication framework for image transmission. At the receiver, a Facebook AI Similarity Search (FAISS) based vector database is constructed by extracting semantic embeddings from images using the Contrastive Language-Image Pre-Training (CLIP) model. During transmission, the transmitter first extracts a 512-dimensional semantic feature using the CLIP model, then compresses it with a lightweight neural network for transmission. After receiving the signal, the receiver reconstructs the feature back to 512 dimensions and performs similarity matching from the KB to retrieve the most semantically similar image. Semantic transmission success is determined by category consistency between the transmitted and retrieved images, rather than traditional metrics like Peak Signal-to-Noise Ratio (PSNR). The proposed system prioritizes semantic accuracy, offering a new evaluation paradigm for semantic-aware communication systems. Experimental validation on CIFAR100 demonstrates the effectiveness of the framework in achieving semantic image transmission. |
| title | Knowledge-Base based Semantic Image Transmission Using CLIP |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.01053 |