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Loss-Resilient Semantic Communication over Packet-Loss Networks at Extreme-Low Bandwidth — arXiv:2608.19590

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19590
License (as archived): CC0-1.0
Archived: 2026-10-18T13:42:44.975Z
SHA-256 of the archived text: 2161f61286fee310a033669e34972f4f5baf9717a44ddfdc10020403165835d9
Stable URL of this page: https://metraipolis.com/documento/dc_ea2dfbb7afe8

How to cite

Loss-Resilient Semantic Communication over Packet-Loss Networks at Extreme-Low Bandwidth — arXiv:2608.19590 — arXiv (en), https://arxiv.org/abs/2608.19590 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea2dfbb7afe8 (Metraipolis archive dc_ea2dfbb7afe8, sha256 2161f61286fee310a033669e34972f4f5baf9717a44ddfdc10020403165835d9)

BibTeX

@misc{dcea2dfbb7afe8,
  title = {Loss-Resilient Semantic Communication over Packet-Loss Networks at Extreme-Low Bandwidth — arXiv:2608.19590},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19590},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea2dfbb7afe8; sha256 2161f61286fee310a033669e34972f4f5baf9717a44ddfdc10020403165835d9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19590. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In extreme-low bandwidth network scenarios, generative semantic codecs have emerged as promising solutions to reduce bandwidth cost for visual communications. However, these learned codecs are usually optimized solely for compression efficiency and thus not robust against transmission errors. Corruptions due to packet-loss among these highly compact generative latent representations often cause more critical degradation in fidelity and realism, intensified by the severe error propagation across the latent contexts and multi-step decoding process. In this paper, we propose ResiGLC, a novel loss-resilient generative latent coding framework designed for robust semantic communication over extreme-low bandwidth packet-loss networks. Motivated by the inherent goal-consistency between generation and compression, we sufficiently exploit the impressive in-context predictive capabilities of language models. Integrated with the masked learning strategy, our model supports arbitrary context modeling of latent codes, which could mitigate the error propagation and handle unpredictable packet loss patterns. At the receiver, a progressive resilient decoding pipeline is presented, which leverages both the contextual relationship of the latent codes and the multi-modal semantic prior in the generative latent space, separately. By jointly optimizing toward both compression efficiency and packet-loss resilience, our proposed progressive decoding mechanism offers graceful performance when dealing with dynamic packet losses. Through extensive experimental evaluations, we establish that under packet-loss network conditions, ResiGLC can effectively improve the loss-resilience in terms of perceptual fidelity and realism qualities with extreme-low bandwidth cost.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-21 in eess.IV).
Categorie arXiv: Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2608.19590 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:40:13.354Z dalla pagina https://arxiv.org/catchup/eess.IV/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).