Towards Loss-Resilient Image Coding for Unstable Satellite Networks

• URL: http://arxiv.org/abs/2501.11263v1
• Date: Mon, 20 Jan 2025 04:11:09 GMT
• Title: Towards Loss-Resilient Image Coding for Unstable Satellite Networks
• Authors: Hongwei Sha, Muchen Dong, Quanyou Luo, Ming Lu, Hao Chen, Zhan Ma,
• Abstract summary: Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. unstable satellite networks, particularly those with frequent packet loss, present a severe challenge to accurate image transmission. We propose a loss-resilient image coding approach that leverages end-to-end optimization in learned image compression.
• Score: 24.752636739603116
• License:
• 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.

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