ReLViC：采用分散分组与可控分组依赖的抗损失学习视频编码

中文摘要 AI 辅助

分组丢失会严重损害学习视频编码的性能，因为丢失的隐式令牌会同时影响空间重建和时间预测。本文提出ReLViC，一种抗损失学习视频编码框架，可联合解决隐式编码和分组丢失恢复问题。ReLViC将空间相邻的隐式令牌分散到不同分组中，并采用双用途Transformer，在编码时估计熵模型参数，在接收端重建丢失的隐式令牌。它通过分段长度参数化的周期重置分组上下文拓扑来控制分组依赖，从而在不重新训练的情况下调整压缩效率与错误传播范围之间的权衡。采用三阶段渐进式训练流程，依次建立单帧编码、学习用于熵建模的时间上下文，最后在模拟分组丢失下优化被掩码隐式令牌的恢复。使用突发丢失轨迹进行实验，对比ReLViC与采用里德-所罗门前向纠错（FEC）保护的H.265，以及抗损失学习视频编解码器GRACE。结果显示，在严重分组丢失场景下，ReLViC的重建更稳定，且性能优于上述两种基线方法。

英文摘要

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.

发表机构

• College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）
• Shenzhen CyberAray Network Technology Company Ltd.（深圳赛博阵列网络科技有限公司）
• Department of Electrical and Computer Engineering, University of Delaware（特拉华大学电气与计算机工程系）

机构由 AI 辅助整理，请以论文原文为准。

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13 pages