ReLViC通过分散编码块和可控依赖，提升视频传输在丢包下的稳定性。
ReLViC: Loss-Resilient Learned Video Coding with Dispersed Packetization and Controllable Packet Dependencies
- 将相邻的隐变量分散到不同数据包，降低丢包影响范围。
- 在严重丢包下重建质量优于H.265+FEC和GRACE基准模型。
- 无需重训练即可调节压缩效率与错误传播的平衡，适合实时视频传输。
丢包会严重损害学习型视频编码性能，因为缺失的隐变量会影响空间重建和时间预测。本文提出ReLViC，一种联合处理隐变量编码与丢包恢复的鲁棒性学习视频编码框架。ReLViC将空间相邻的隐变量分散至不同数据包，并采用双用途Transformer，在编码时估计熵模型参数，在接收端重构丢失的隐变量。通过周期性重置的数据包上下文拓扑结构，由片段长度参数化，可调控压缩效率与错误传播范围，且无需重新训练。采用三阶段渐进式训练：先建立单帧编码，再学习时间上下文用于熵建模，最后在模拟丢包条件下优化掩码隐变量的恢复能力。基于突发丢包测试序列的实验表明，ReLViC在严重丢包场景下重建更稳定，性能优于采用前向纠错（FEC）保护的H.265及另一款鲁棒学习视频编解码器GRACE。
原文摘要 · Abstract (English)
Packet loss can severely impair learned video coding because missing latent tokens compromise both spatial reconstruction and temporal prediction. We present ReLViC, a loss-resilient learned video coding framework that jointly addresses latent coding and packet-loss recovery. ReLViC disperses spatially adjacent latent tokens across packets and employs a dual-purpose Transformer to estimate entropy-model parameters during coding and reconstruct missing latent tokens at the receiver. It controls packet dependencies through a periodic-reset packet-context topology parameterized by the segment length, thereby tuning the trade-off between compression efficiency and error-propagation range without retraining. A three-stage progressive training procedure establishes single-frame coding, learns temporal context for entropy modeling, and then optimizes the recovery of masked latent tokens under simulated packet loss. Experiments using burst-loss traces evaluate ReLViC against H.265 protected by Reed--Solomon forward error correction (FEC) and GRACE, a loss-resilient learned video codec. ReLViC delivers more stable reconstruction and outperforms both baselines under severe packet loss.
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