Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Yuhang Wei (Shanghai Jiao Tong University), Chuqin Zhou (Shanghai Jiao Tong University), Yibo Shi (Huawei Technologies Ltd), Jing Wang (Huawei Technologies Ltd), Guo Lu (Shanghai Jiao Tong University)
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-08-12
摘要
arXiv:2608.11096v1 Announce Type: new Abstract: Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications. This vulnerability stems from non-uniform information distribution at the packetization stage and sequential decoding dependencies at the entropy coding stage. We propose an end-to-end loss-resilient image compression scheme that addresses both. Before packetization, we introduce an Inter-Channel Redistribution (ICR) mechanism to redistribute channel energy, preventing critical information concentrating in a small subset of channels. Then, an Interleaved Channel Grouping (ICG) strategy partitions latent channels in a strided manner to disperse information across packets, with each packet kept within constrained sizes.