针对块丢失信道，提出分域神经视频编码，提升传输鲁棒性。
Towards Robust Semantic Video Transmission over Block Erasure Channels
- 分空间与特征域设计，支持局部丢失处理与语义恢复
- 空间域抗随机局部丢失，特征域抗分布式丢失且保真度高
- 适合对鲁棒性要求高的视频通信系统设计参考
本文研究面向块丢失信道的语义感知神经联合源信道编码（JSCC）在视频传输中的应用。提出一种融合空间域与特征域设计的神经视频压缩框架。在空间域，将视频帧划分为块，通过均匀丢失和两级语义引导的非均匀丢失策略实现局部丢失处理与细粒度鲁棒性控制。在特征域，对潜在特征进行划分，使缺失特征可被语义恢复并保持整体空间一致性。大量实验量化了在不同均匀与非均匀丢失概率下的重建质量。结果表明，空间域JSCC擅长应对随机局部丢失，而特征域JSCC在分布式丢失下更具鲁棒性，并在低丢失场景中保持更高保真度。分析揭示了空间连续性与语义冗余之间的权衡，为设计任务感知的鲁棒视频通信系统提供了洞见。
原文摘要 · Abstract (English)
This paper investigates semantic-aware neural joint source-channel coding (JSCC) for robust video transmission over block erasure channels. We propose a neural video compression framework exploring both spatial-domain and feature-domain designs. In the spatial domain, video frames are partitioned into blocks, enabling localized erasure handling and fine-grained robustness control via uniform erasure and two-level, semantic-guided non-uniform erasure strategies. In the feature domain, latent features are partitioned, enabling missing features to be semantically recovered while maintaining overall spatial consistency. Comprehensive experiments quantify reconstruction quality under varying uniform and non-uniform erasure probabilities. Our results show that spatial-domain JSCC excels at handling random localized losses, whereas feature-domain JSCC provides superior robustness to distributed erasures and maintains fidelity under low-loss scenarios. The analysis highlights the trade-offs between spatial continuity and semantic redundancy, offering insights for designing robust, task-aware video communication systems.
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