  Decoding remote sensing images to achieve high perceptual quality,
particularly at low bitrates, remains a significant challenge. To address this
problem, we propose the invertible neural network-based remote sensing image
compression (INN-RSIC) method. Specifically, we capture compression distortion
from an existing image compression algorithm and encode it as a set of
Gaussian-distributed latent variables via INN. This ensures that the
compression distortion in the decoded image becomes independent of the ground
truth. Therefore, by leveraging the inverse mapping of INN, we can input the
decoded image along with a set of randomly resampled Gaussian distributed
variables into the inverse network, effectively generating enhanced images with
better perception quality. To effectively learn compression distortion, channel
expansion, Haar transformation, and invertible blocks are employed to construct
the INN. Additionally, we introduce a quantization module (QM) to mitigate the
impact of format conversion, thus enhancing the framework's generalization and
improving the perceptual quality of enhanced images. Extensive experiments
demonstrate that our INN-RSIC significantly outperforms the existing
state-of-the-art traditional and deep learning-based image compression methods
in terms of perception quality.
