Block Erasure-Aware Semantic Multimedia Compression via JSCC Autoencoder
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908794013876224 |
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| author | Esfahanizadeh, Homa Fayaz, Nargis Du, Jinfeng Viswanathan, Harish |
| author_facet | Esfahanizadeh, Homa Fayaz, Nargis Du, Jinfeng Viswanathan, Harish |
| contents | We present an AI-based framework for semantic transmission of multimedia data over band-limited, time-varying channels. The method targets scenarios where large content is split into multiple packets, with an unknown number potentially dropped due to channel impairments. Using joint source-channel coding (JSCC), our approach achieves reliable semantic reconstruction with graceful quality degradation as channel conditions worsen, eliminating the need for retransmissions that cause unacceptable delays in latency-sensitive applications such as video conferencing and robotic control. The framework is compatible with existing network protocols and further enables intelligent congestion control and unequal error protection. A tunable design parameter allows balancing robustness at low channel quality against fidelity at high channel quality. Experiments demonstrate significant robustness improvement over state-of-the-art baselines in both image and video domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20707 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Block Erasure-Aware Semantic Multimedia Compression via JSCC Autoencoder Esfahanizadeh, Homa Fayaz, Nargis Du, Jinfeng Viswanathan, Harish Multimedia We present an AI-based framework for semantic transmission of multimedia data over band-limited, time-varying channels. The method targets scenarios where large content is split into multiple packets, with an unknown number potentially dropped due to channel impairments. Using joint source-channel coding (JSCC), our approach achieves reliable semantic reconstruction with graceful quality degradation as channel conditions worsen, eliminating the need for retransmissions that cause unacceptable delays in latency-sensitive applications such as video conferencing and robotic control. The framework is compatible with existing network protocols and further enables intelligent congestion control and unequal error protection. A tunable design parameter allows balancing robustness at low channel quality against fidelity at high channel quality. Experiments demonstrate significant robustness improvement over state-of-the-art baselines in both image and video domains. |
| title | Block Erasure-Aware Semantic Multimedia Compression via JSCC Autoencoder |
| topic | Multimedia |
| url | https://arxiv.org/abs/2601.20707 |