Fixed-Quality Compression of Remote Sensing Images With Neural Networks
Mijares i Verdú, Sebastià (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Chabert, Marie (Université de Toulouse)
Oberlin, Thomas (Université de Toulouse)
Serra-Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)

Date:	2024
Abstract:	Fixed-quality image compression is a coding paradigm where the tolerated introduced distortion is set by the user. This article proposes a novel fixed-quality compression method for remote sensing images. It is based on a neural architecture we have recently proposed for multirate satellite image compression. In this article, we show how to efficiently estimate the reconstruction quality using an appropriate statistical model. The performance of our approach is assessed and compared against recent fixed-quality coding techniques and standards in terms of accuracy and rate-distortion, as well as with recent machine learning compression methods in rate-distortion, showing competitive results. In particular, the proposed method does not introduce artifacts even when coding neighboring areas at different qualities.
Grants:	Agencia Estatal de Investigación PID2021-125258OB-I00 Agencia Estatal de Investigación PRE2019-088824 Generalitat de Catalunya 2021/SGR-00643
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Data compression ; Neural network applications ; Neural networks ; Optical data processing ; Remote sensing
Published in:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17 (July 2024) , p. 12169-12180, ISSN 2151-1535

DOI: 10.1109/JSTARS.2024.3422215

12 p, 14.9 MB

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