  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.
