{
  "abstract": "Neural joint source-channel coding (JSCC) enables efficient image transmission over noisy channels, yet distortion-optimized approaches often produce blurry reconstructions. While diffusion priors can enhance visual fidelity, their iterative sampling incurs substantial decoding latency. To address this limitation, we propose OSD-JSCC, an SNR-adaptive JSCC framework with a single diffusion step. Specifically, OSD-JSCC transmits pretrained variational autoencoder (VAE) latents through an SNR-conditioned JSCC codec and incorporates two key designs. First, we introduce a stochastic diffusion-state interface that enables the JSCC decoder to predict a Gaussian diffusion state rather than a clean latent, while extracting image-specific semantic tokens. The sampled diffusion state preserves spatial structure and provides a good initialization for one-step refinement, whereas the semantic tokens, together with an SNR token, guide the reconstruction process. Second, we develop SNR-conditioned feature modulation and adaptive timestep selection to jointly adapt latent transmission and diffusion refinement to varying channel conditions. Furthermore, we adopt a three-stage training strategy with low-rank adaptation (LoRA) to efficiently adapt the pretrained diffusion prior to the JSCC task. Extensive experiments on Kodak and CLIC2020 demonstrate that OSD-JSCC achieves superior perceptual quality and decoding efficiency. Compared with existing diffusion-based JSCC baselines, OSD-JSCC reduces LPIPS by approximately 43% on Kodak and 53% on CLIC2020, while achieving up to a 50.6$\\times$ reduction in decoding latency.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.06437",
  "author_total": 2,
  "authors": [
    {
      "name": "Shunpu Tang"
    },
    {
      "name": "Qianqian Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8sg2w6x7ag9wk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06437",
  "fulltext_url": null,
  "id": "8sg2w6x7ag9wk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06437",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06437",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8sg2w6x7ag9wk.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:21:00Z",
      "native_id": "2611.06437",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06437"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "OSD-JSCC: Neural Joint Source-Channel Coding with One-Step Diffusion",
  "title_text": "OSD-JSCC: Neural Joint Source-Channel Coding with One-Step Diffusion",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:30Z",
  "url": "https://research.draconitic.com/paper/8sg2w6x7ag9wk",
  "venue": "arXiv",
  "versions": []
}