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Learned Structure-Based Hybrid Framework for Martian Image Compression
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AI
DOI：10.1109/LGRS.2023.3327687.png)
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Abstract
En 中文
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.
Keywords:
Image coding
Image reconstruction
Standards
Transform coding
Image restoration
Convolution
Mars
Deep neural network (DNN)
learning-based image compression
Martian image compression (MIC)
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