针对卫星网络丢包问题，提出一种抗丢包图像编码方法。
Towards Loss-Resilient Image Coding for Unstable Satellite Networks
- 采用端到端优化的渐进式编码框架，提升鲁棒性。
- 在不同丢包率下，重建质量优于传统与深度学习方法。
- 适合应急短时数据传输等不稳定网络场景使用。
地球静止轨道（GEO）卫星通信在应急短时数据服务中具有显著优势。然而，频繁丢包的不稳定卫星网络对图像准确传输构成严峻挑战。为此，我们提出一种基于学习图像压缩（LIC）端到端优化的抗丢包图像编码方法。该方法基于通道渐进编码框架，在编码器侧引入空间-通道重排（SCR），在解码器侧采用掩码条件聚合（MCA），以改善不可预测错误下的重建质量。通过将吉尔伯特-埃利奥特模型融入训练过程，增强模型在真实网络条件下的泛化能力。大量实验表明，该方法在不同丢包率下均优于传统及深度学习方法，在复杂环境中实现稳定高效的渐进传输。代码已开源：https://github.com/NJUVISION/LossResilientLIC。
原文摘要 · Abstract (English)
Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. However, unstable satellite networks, particularly those with frequent packet loss, present a severe challenge to accurate image transmission. To address it, we propose a loss-resilient image coding approach that leverages end-to-end optimization in learned image compression (LIC). Our method builds on the channel-wise progressive coding framework, incorporating Spatial-Channel Rearrangement (SCR) on the encoder side and Mask Conditional Aggregation (MCA) on the decoder side to improve reconstruction quality with unpredictable errors. By integrating the Gilbert-Elliot model into the training process, we enhance the model's ability to generalize in real-world network conditions. Extensive evaluations show that our approach outperforms traditional and deep learning-based methods in terms of compression performance and stability under diverse packet loss, offering robust and efficient progressive transmission even in challenging environments. Code is available at https://github.com/NJUVISION/LossResilientLIC.
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