Multi-spectral Entropy Constrained Neural Compression of Solar Imagery
This work addresses the need for efficient image compression in solar missions, offering a domain-specific improvement over existing techniques.
The paper tackles the problem of compressing multi-spectral solar imagery for efficient transmission by proposing a transformer-based neural compressor that captures intra- and inter-wavelength redundancies, outperforming conventional methods and improving decorrelation across wavelengths.
Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmission efficient and feasible, image compression systems need to be exploited. Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner. In this work we have proposed a transformer-based multi-spectral neural image compressor to efficiently capture redundancies both intra/inter-wavelength. To unleash the locality of window-based self attention mechanism, we propose an inter-window aggregated token multi head self attention. Additionally to make the neural compressor autoencoder shift invariant, a randomly shifted window attention mechanism is used which makes the transformer blocks insensitive to translations in their input domain. We demonstrate that the proposed approach not only outperforms the conventional compression algorithms but also it is able to better decorrelates images along the multiple wavelengths compared to single spectral compression.