Filtered Residual Compression for Satellite Images
Résumé
Learned image compression neural networks have difficulties adapting to certain satellite image characteristics, especially high frequencies that disappear at a high bit-rate in the blur generated in the reconstruction. To answer this problem we describe a joint end-to-end trainable neural network. It is separated into a general compression network and a smaller specialised network. We train a specialized network to compress the residual part of the image to best preserve the highfrequency details present in the satellite images. The proposed model achieves higher rate-distortion performance than current lossy image compression standards and also manages to retrieve details previously poorly reconstructed.
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