A Quantization Loss Compensation Network for Remote Sensing Image Compression

High-resolution remote sensing images (HRRSIs) contain abundant details and texture information. Existing lossy compression methods employ quantization to eliminate redundant information, but this leads to irreversible effects on the subtle details of HRRSIs. This paper proposes a quantization loss compensation network to address this issue. We use a trainable variational autoencoder to learn the details and texture information of HRRSIs from the error between pre-and post-quantized latent representations. During the encoding of HRRSIs, the quantization errors are inputted into the encoding module of the compensation network to generate bitstreams. When it comes to image decoding, using the decoding module of the compensation network to generate quantization loss compensation information, which, together with the quantization latent representations, contributes to the HRRSIs reconstruction. To validate the effectiveness of our approach in reconstructing details of HRRSIs, we conducted experiments on two remote sensing datasets. The experimental results also indicate that our method exhibits superior compression performance.