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.

翻译：研究太阳动态行为的任务旨在捕获太阳的多光谱图像，并每日将其传输至地面站。为提升传输效率与可行性，需开发图像压缩系统。近年来，基于端到端优化的神经网络图像压缩系统展现出在专用场景中的巨大潜力。本研究提出一种基于Transformer的多光谱神经图像压缩器，可高效捕获波长内与波长间的冗余信息。为释放基于窗口的自注意力机制的局部性，我们提出一种窗口间聚合令牌多头自注意力机制。此外，为使神经压缩自编码器具备平移不变性，采用随机移位窗口注意力机制，使Transformer模块对其输入域的平移不敏感。实验表明，所提方法不仅优于传统压缩算法，且相比单光谱压缩，能更好地解相关多波长图像。