---
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标题: "Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression"
原文链接: "https://arxiv.org/abs/2608.11096"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.11096"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression

Authors:[Yuhang Wei](https://arxiv.org/search/cs?searchtype=author&query=Wei,+Y) (1), [Chuqin Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+C) (1), [Yibo Shi](https://arxiv.org/search/cs?searchtype=author&query=Shi,+Y) (2), [Jing Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+J) (2), [Guo Lu](https://arxiv.org/search/cs?searchtype=author&query=Lu,+G) (1) ((1) Shanghai Jiao Tong University, (2) Huawei Technologies Ltd.)

[View PDF](https://arxiv.org/pdf/2608.11096) [HTML (experimental)](https://arxiv.org/html/2608.11096v1)

> 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. To limit cascading errors from lost packets, we adopt a two-layer dual-branch autoregressive structure to shorten the dependency chain. Extensive experiments demonstrate that our method consistently outperforms existing approaches in both reconstruction quality and stability. At 20% packet loss, it achieves an average PSNR gain of 1.84 dB over LossResilientLIC while reducing PSNR variance by an order of magnitude. Notably, trained under uniform random loss only, our model generalizes to bursty loss modeled by the Gilbert-Elliott channel, outperforming methods explicitly trained for such conditions.

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| ACM classes: | I.4.2; I.4.10; I.2.6 |
| Cite as: | [arXiv:2608.11096](https://arxiv.org/abs/2608.11096) \[cs.CV\] |
|  | (or [arXiv:2608.11096v1](https://arxiv.org/abs/2608.11096v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11096](https://doi.org/10.48550/arXiv.2608.11096) |
| Related DOI: | [https://doi.org/10.1145/3767308.3836205](https://doi.org/10.1145/3767308.3836205) |

## Submission history

From: Yuhang Wei \[[view email](https://arxiv.org/show-email/ef369e64/2608.11096)\]  
**\[v1\]** Tue, 11 Aug 2026 16:01:03 UTC (2,718 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11096) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
