  Deep learning-based methods have garnered significant attention in remote
sensing (RS) image compression due to their superior performance. Most of these
methods focus on enhancing the coding capability of the compression network and
improving entropy model prediction accuracy. However, they typically compress
and decompress each image independently, ignoring the significant inter-image
similarity prior. In this paper, we propose a codebook-based RS image
compression (Code-RSIC) method with a generated discrete codebook, which is
deployed at the decoding end of a compression algorithm to provide inter-image
similarity prior. Specifically, we first pretrain a high-quality discrete
codebook using the competitive generation model VQGAN. We then introduce a
Transformer-based prediction model to align the latent features of the decoded
images from an existing compression algorithm with the frozen high-quality
codebook. Finally, we develop a hierarchical prior integration network (HPIN),
which mainly consists of Transformer blocks and multi-head cross-attention
modules (MCMs) that can query hierarchical prior from the codebook, thus
enhancing the ability of the proposed method to decode texture-rich RS images.
Extensive experimental results demonstrate that the proposed Code-RSIC
significantly outperforms state-of-the-art traditional and learning-based image
compression algorithms in terms of perception quality. The code will be
available at \url{https://github.com/mlkk518/Code-RSIC/
