Towards Loss-Resilient Image Coding for Unstable Satellite Networks
Journal:
arXiv
Published Date:
Jan 20, 2025
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.
