Exploring Distortion Prior with Latent Diffusion Models for Remote Sensing Image Compression

TL;DR

This paper introduces a novel remote sensing image compression method using latent diffusion models to leverage distortion priors, significantly improving compression quality and efficiency over existing algorithms.

Contribution

It proposes a two-stage LDM-based framework that utilizes distortion priors from high-quality images and enhances decoding with a Transformer-based network, advancing remote sensing image compression.

Findings

Outperforms state-of-the-art algorithms in subjective and objective metrics

Achieves 32% bit savings on DOTA dataset with JPEG2000

Effectively utilizes distortion prior to enhance image quality

Abstract

Deep learning-based image compression algorithms typically focus on designing encoding and decoding networks and improving the accuracy of entropy model estimation to enhance the rate-distortion (RD) performance. However, few algorithms leverage the compression distortion prior from existing compression algorithms to improve RD performance. In this paper, we propose a latent diffusion model-based remote sensing image compression (LDM-RSIC) method, which aims to enhance the final decoding quality of RS images by utilizing the generated distortion prior from a LDM. Our approach consists of two stages. In the first stage, a self-encoder learns prior from the high-quality input image. In the second stage, the prior is generated through an LDM, conditioned on the decoded image of an existing learning-based image compression algorithm, to be used as auxiliary information for generating the texture-rich enhanced image. To better utilize the prior, a channel attention and gate-based dynamic feature attention module (DFAM) is embedded into a Transformer-based multi-scale enhancement network (MEN) for image enhancement. Extensive experiments demonstrate the proposed LDM-RSIC significantly outperforms existing state-of-the-art traditional and learning-based image compression algorithms in terms of both subjective perception and objective metrics. Additionally, we use the LDM-based scheme to improve the traditional image compression algorithm JPEG2000 and obtain 32.00% bit savings on the DOTA testing set. The code will be available at https://github.com/mlkk518/LDM-RSIC.