提出新框架，提升无线视频传输在复杂信道下的画质与稳定性。
Robust Deep Joint Source-Channel Coding for Video Transmission over Multipath Fading Channel
- 融合时序冗余建模与鲁棒调制编码，提升抗多径衰落能力
- 在恶劣信道下实现平均5.13 dB的重建质量提升
- 适合无线视频实时传输、低延迟场景应用
针对多径衰落信道下的无线视频传输挑战，本文提出一种鲁棒的深度联合信源信道编码（DeepJSCC）框架。通过有效利用时序冗余，并在调制、编码和解码阶段引入鲁棒创新，显著提升传输性能。调制阶段采用定制化正交频分复用（OFDM），将宽带信号分解为正交平坦子信道，有效缓解频率选择性衰落；编码阶段引入基于多尺度高斯扭曲特征的条件上下文编码，高效建模时序冗余，在严苛带宽约束下大幅提高重建质量；解码阶段集成轻量级去噪模块，简化信号恢复过程，加速收敛，缓解信道估计、均衡与语义重建同时进行带来的次优与收敛慢问题。实验表明，所提框架在复杂多径衰落条件下，相比现有先进视频DeepJSCC方法，平均重建质量提升5.13 dB。
原文摘要 · Abstract (English)
To address the challenges of wireless video transmission over multipath fading channels, we propose a robust deep joint source-channel coding (DeepJSCC) framework by effectively exploiting temporal redundancy and incorporating robust innovations at the modulation, coding, and decoding stages. At the modulation stage, tailored orthogonal frequency division multiplexing (OFDM) for robust video transmission is employed, decomposing wideband signals into orthogonal frequency-flat sub-channels to effectively mitigate frequency-selective fading. At the coding stage, conditional contextual coding with multi-scale Gaussian warped features is introduced to efficiently model temporal redundancy, significantly improving reconstruction quality under strict bandwidth constraints. At the decoding stage, a lightweight denoising module is integrated to robustly simplify signal restoration and accelerate convergence, addressing the suboptimality and slow convergence typically associated with simultaneously performing channel estimation, equalization, and semantic reconstruction. Experimental results demonstrate that the proposed robust framework significantly outperforms state-of-the-art video DeepJSCC methods, achieving an average reconstruction quality gain of 5.13 dB under challenging multipath fading channel conditions.
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