Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission
Emerging physical AI systems require low-latency, task-oriented video communication over unreliable channels. We propose a semantic-aware multi-level neural video coding method for robust low-latency video transmission over unreliable channels that are abstracted as multi-level packet erasure channels. Built upon the real-time DCVC-RT neural video codec, the proposed framework introduces a semantic- and feature-aware coding strategy that partitions encoded representations into packets carrying different levels of semantic and latent-feature importance and assigns these packets to different streams, each associated with a priority level when transmitted over unreliable communication channe...
| Authors | Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du et al. |
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| Primary category | eess.IV |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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