Learned Structure-Based Hybrid Framework for Martian Image Compression

Recent landing marches on Mars have enabled the access to Martian surface images, which act as an important vehicle to demystify the evolution and habitability of Mars, in terms of climate, geography, etc. Transmitting Martian images thus calls for efficient compression methods to ensure the high-qu...

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Vydané v:IEEE geoscience and remote sensing letters Ročník 20; s. 1 - 5
Hlavní autori: Li, Shengxi, Sun, Xiancheng, Xu, Mai, Jiang, Lai, Zou, Xin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Predmet:
Communication
Compression
Geography
Habitability
Image compression
Image transmission
Mars
Mars landing
Mars surface
ISSN:1545-598X, 1558-0571
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Shrnutí:Recent landing marches on Mars have enabled the access to Martian surface images, which act as an important vehicle to demystify the evolution and habitability of Mars, in terms of climate, geography, etc. Transmitting Martian images thus calls for efficient compression methods to ensure the high-quality reconstruction from distant communication, in which the research is yet to start. To address this issue, we propose in this letter a learned structure-based hybrid (LSH) framework to compress Martian images. More specifically, we first observe that the structural consistency exists across Martian images, which motivates us to propose a structural compression network (SCN). The aim of SCN is to compactly represent the structural information of Martian images, thus allowing for the compression at extremely low bit-rates. Then, we propose a detail compensation network (DCN) to reconstruct the missing details when we restore from the structural information, which benefits from improved compression efficiency by reduced bit-rates. The experimental results have verified the superior performances of our LSH method on compressing Martian images, against existing state-of-the-art methods.
Bibliografia:ObjectType-Article-1
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3327687