标题： ResiTok：一种支持弹性令牌化的超低速率和鲁棒图像传输框架 显示英文标题

标题： ResiTok: A Resilient Tokenization-Enabled Framework for Ultra-Low-Rate and Robust Image Transmission

摘要： 实时传输无线网络中的视觉数据仍然极具挑战性，即使利用先进的深度神经网络，在有限带宽和弱连接等严重信道条件下尤其如此。 在本文中，我们提出了一种新颖的弹性标记化使能（ResiTok）框架，旨在实现超低码率图像传输，同时保持高重建质量并具备卓越的鲁棒性。 通过将视觉信息重新组织为由关键标记和补充细节标记组成的分层标记组，ResiTok 实现了在受限信道条件下的渐进编码和视觉质量的优雅降级。 我们的一个重要贡献是一种与专门零出训练策略集成的弹性一维标记化方法，该方法在训练过程中系统地模拟标记丢失，从而赋予神经网络从不完整标记集中有效压缩和重构图像的能力。 此外，自适应信道编码和调制设计根据当前信道条件动态分配编码资源，在极低信道带宽比下仍能提供优越的语义保真度和结构一致性。 评估结果显示，ResiTok 在语义相似性和视觉质量方面优于最先进的方法，并且在具有挑战性的信道条件下具有显著优势。 显示英文摘要

摘要： Real-time transmission of visual data over wireless networks remains highly challenging, even when leveraging advanced deep neural networks, particularly under severe channel conditions such as limited bandwidth and weak connectivity. In this paper, we propose a novel Resilient Tokenization-Enabled (ResiTok) framework designed for ultra-low-rate image transmission that achieves exceptional robustness while maintaining high reconstruction quality. By reorganizing visual information into hierarchical token groups consisting of essential key tokens and supplementary detail tokens, ResiTok enables progressive encoding and graceful degradation of visual quality under constrained channel conditions. A key contribution is our resilient 1D tokenization method integrated with a specialized zero-out training strategy, which systematically simulates token loss during training, empowering the neural network to effectively compress and reconstruct images from incomplete token sets. Furthermore, the channel-adaptive coding and modulation design dynamically allocates coding resources according to prevailing channel conditions, yielding superior semantic fidelity and structural consistency even at extremely low channel bandwidth ratios. Evaluation results demonstrate that ResiTok outperforms state-of-the-art methods in both semantic similarity and visual quality, with significant advantages under challenging channel conditions.