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

• URL: http://arxiv.org/abs/2608.11096v1
• Date: Tue, 11 Aug 2026 16:01:03 GMT
• Title: Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression
• Abstract summary: We propose an end-to-end loss-resilient image compression scheme that addresses packet loss.<n>Our method consistently outperforms existing approaches in both reconstruction quality and stability.<n> Notably, trained under uniform random loss only, our model generalizes to bursty loss modeled by the Gilbert-Elliott channel.
• Score: 18.227887897767385
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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