Joint Compression and Demosaicking For Satellite Images
Pascal Bacchus, Renaud Fraisse, Aline Roumy, Christine Guillemot
Abstract
Image sensors used in real camera systems are equipped with colour filter arrays which sample the light rays in different spectral bands. Each colour channel can thus be obtained sep-arately by considering the corresponding colour filter. While existing compression solutions mostly assume that the captured raw data has been demosaicked prior to compression, in this paper, we describe an end-to-end trainable neural network for joint compression and demosaicking of satellite images. We first introduce a training loss combining a perceptual loss with the classical mean square error, which is shown to better preserve the high-frequency details present in satellite images. We then present a multi-loss balancing strategy which significantly improves the performance of the proposed joint demosaicking-compression solution.
BibTeX
@inproceedings{icassp2023_jointcompression,
title = {Joint Compression and Demosaicking For Satellite Images},
author = {Pascal Bacchus and Renaud Fraisse and Aline Roumy and Christine Guillemot},
booktitle = {ICASSP 2023},
year = {2023}
}