Efficient Lossy Satellite Image Compression Using Hybrid Autoencoder Model

What is it about?

The challenge of handling the vast amount of data generated by onboard satellite instruments arises due to limited bandwidth and memory capacity, which must be managed cautiously. Image compression is used as a tool to minimize the data size for easier storage and transmission between ground stations. An efficient compression method maintains the quality of the source representation in the reconstruction stage. Conventional lossy image compression techniques have been used for the last few decades. However, recent learning-based approaches have gained substantial interest in the artificial intelligence field of research that achieves highly promising image reconstruction results under adequate storage, and low bandwidth.

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