Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression

TL;DR

This paper introduces a novel remote sensing image compression method that leverages a prebuilt codebook and Transformer models to utilize inter-image similarity, significantly improving perceptual quality over existing methods.

Contribution

It proposes a codebook-based compression framework with a Transformer prediction model and hierarchical prior integration, exploiting inter-image similarity for enhanced RS image compression.

Findings

Outperforms state-of-the-art methods in perception quality

Utilizes a high-quality codebook pretrained with VQGAN

Employs Transformer-based models for hierarchical prior querying

Abstract

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/