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Article

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

1
IRIT/INP-ENSEEIHT, University of Toulouse, 31071 Toulouse, France
2
Telecommunications for Space and Aeronautics (TéSA) Laboratory, 31500 Toulouse, France
3
ISAE-SUPAERO, University of Toulouse, 31055 Toulouse, France
4
CNES, 31400 Toulouse, France
5
Thales Alenia Space, 06150 Cannes, France
6
ESA, 2201 AZ Noordwijk, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Vladimir Lukin
Remote Sens. 2021, 13(3), 447; https://doi.org/10.3390/rs13030447
Received: 18 December 2020 / Revised: 21 January 2021 / Accepted: 22 January 2021 / Published: 27 January 2021
(This article belongs to the Special Issue Remote Sensing Data Compression)
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. View Full-Text
Keywords: remote sensing; lossy compression; on board compression; transform coding; rate-distortion; JPEG2000; CCSDS; learned compression; neural networks; variational autoencoder; complexity remote sensing; lossy compression; on board compression; transform coding; rate-distortion; JPEG2000; CCSDS; learned compression; neural networks; variational autoencoder; complexity
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MDPI and ACS Style

Alves de Oliveira, V.; Chabert, M.; Oberlin, T.; Poulliat, C.; Bruno, M.; Latry, C.; Carlavan, M.; Henrot, S.; Falzon, F.; Camarero, R. Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression. Remote Sens. 2021, 13, 447. https://doi.org/10.3390/rs13030447

AMA Style

Alves de Oliveira V, Chabert M, Oberlin T, Poulliat C, Bruno M, Latry C, Carlavan M, Henrot S, Falzon F, Camarero R. Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression. Remote Sensing. 2021; 13(3):447. https://doi.org/10.3390/rs13030447

Chicago/Turabian Style

Alves de Oliveira, Vinicius, Marie Chabert, Thomas Oberlin, Charly Poulliat, Mickael Bruno, Christophe Latry, Mikael Carlavan, Simon Henrot, Frederic Falzon, and Roberto Camarero. 2021. "Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression" Remote Sensing 13, no. 3: 447. https://doi.org/10.3390/rs13030447

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