Towards Loss-Resilient Image Coding for Unstable Satellite Networks

TL;DR

This paper introduces a novel loss-resilient image coding method for unstable satellite networks, improving robustness and quality of image transmission under packet loss conditions using learned compression techniques.

Contribution

It proposes a new end-to-end optimized LIC framework with channel-wise progressive coding, SCR, MCA, and Gilbert-Elliot model integration for enhanced loss resilience.

Findings

Outperforms traditional methods in compression and stability.

Effective in diverse packet loss scenarios.

Provides robust progressive image transmission.

Abstract

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.