Asymmetric Multi-Scale Dilated Attention based Network for Remote Sensing Image Compression
Haoyi Ma, Peicheng Zhou, Jia Jia, Ran Tang, Keyan Wang, Yunsong Li · 2024
Recently, there has been rapid development in learned image compression techniques. However, deep learning-based remote sensing image compression algorithms has limitations such as incomplete feature extraction and poor fitting of information entropy distribution. To alleviate these limitations, we propose a remote sensing image compression network based on a multiscale asymmetric codec. Based on hyperprior archi-tecture, we incorporate a multiscale asymmetric codec to extract multiscale features and fit the distribution of latent features through an adaptive context entropy model. Experimental results on the DIOR dataset demonstrate that our method can effectively enhance the visual quality of reconstructed images and achieve high-fidelity at a lower bitrate.