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Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression

Alves de Oliveira, Vinicius and Chabert, Marie and Oberlin, Thomas and Poulliat, Charly and Bruno, Mickael and Latry, Christophe and Carlavan, Mikael and Henrot, Simon and Falzon, Frederic and Camarero, Roberto Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression. (2021) Remote Sensing, 13 (3). 447. ISSN 2072-4292

(Document in English)

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Official URL: https://doi.org/10.3390/rs13030447


Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

Item Type:Article
Additional Information:This work has been carried out under the financial support of the French space agency CNES and Thales Alenia Space. Part of this work has been funded by the Institute for Artificial and Natural Intelligence Toulouse (ANITI) under grant agreement ANR-19-PI3A-0004.
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National d'Études Spatiales - CNES (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Thales (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > ESA - ESTEC (NETHERLANDS)
French research institutions > Artificial and Natural Intelligence Toulouse Institute - ANITI (FRANCE)
Other partners > Laboratoire de recherche en télécommunications spatiales et aéronautiques - TéSA (FRANCE)
Laboratory name:
ANITI ANR-19-PI3A-0004
Deposited On:21 Oct 2021 09:33

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