Text-Guided Token Communication for Wireless Image Transmission
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911045194350592 |
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| author | Liu, Bole Qiao, Li Wang, Ye Gao, Zhen Ma, Yu Ying, Keke Qin, Tong |
| author_facet | Liu, Bole Qiao, Li Wang, Ye Gao, Zhen Ma, Yu Ying, Keke Qin, Tong |
| contents | With the emergence of 6G networks and proliferation of visual applications, efficient image transmission under adverse channel conditions is critical. We present a text-guided token communication system leveraging pre-trained foundation models for wireless image transmission with low bandwidth. Our approach converts images to discrete tokens, applies 5G NR polar coding, and employs text-guided token prediction for reconstruction. Evaluations on ImageNet show our method outperforms Deep Source Channel Coding with Attention Modules (ADJSCC) in perceptual quality and semantic preservation at Signal-to-Noise Ratios (SNRs) above 0 dB while mitigating the cliff effect at lower SNRs. Our system requires no scenario-specific retraining and exhibits superior cross-dataset generalization, establishing a new paradigm for efficient image transmission aligned with human perceptual priorities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-Guided Token Communication for Wireless Image Transmission Liu, Bole Qiao, Li Wang, Ye Gao, Zhen Ma, Yu Ying, Keke Qin, Tong Information Theory With the emergence of 6G networks and proliferation of visual applications, efficient image transmission under adverse channel conditions is critical. We present a text-guided token communication system leveraging pre-trained foundation models for wireless image transmission with low bandwidth. Our approach converts images to discrete tokens, applies 5G NR polar coding, and employs text-guided token prediction for reconstruction. Evaluations on ImageNet show our method outperforms Deep Source Channel Coding with Attention Modules (ADJSCC) in perceptual quality and semantic preservation at Signal-to-Noise Ratios (SNRs) above 0 dB while mitigating the cliff effect at lower SNRs. Our system requires no scenario-specific retraining and exhibits superior cross-dataset generalization, establishing a new paradigm for efficient image transmission aligned with human perceptual priorities. |
| title | Text-Guided Token Communication for Wireless Image Transmission |
| topic | Information Theory |
| url | https://arxiv.org/abs/2507.05781 |