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.

JPEG, a landmark lossy image compression standard, has been widely used over the past years, yet its simple design disadvantages the compression performance compared to other advanced algorithms currently available. This has led to the development of lossless JPEG recompression techniques to further exploit the frequency domain redundancy by reconstructing the binary streams without loss. However, existing work has not considered the lossless recompression problem of similar images, as it can utilize the mutual correlation between transformed coefficients to squeeze additional bits saving. As a result, this paper proposes a lossless recompression method for similar images through the block matching in frequency domain. Specifically, we fulfill the purpose by firstly applying the Controllable Similar Block Search (CSBS) to find the corresponding similar blocks in the transform domain. Built upon the similarity assessment, we deploy both the well-designed Optimal Motion Vector Coding (OMVC) and Variable-Length Residual Coding (VLRC) to improve the recompression efficiency. Experimental results demonstrate the superior performance of our method when compared to other popular JPEG recompression algorithms. To the best of our knowledge, this is the first lossless recompression work for similar images in the frequency domain.