  NASA's Solar Dynamics Observatory (SDO) mission gathers 1.4 terabytes of data
each day from its geosynchronous orbit in space. SDO data includes images of
the Sun captured at different wavelengths, with the primary scientific goal of
understanding the dynamic processes governing the Sun. Recently, end-to-end
optimized artificial neural networks (ANN) have shown great potential in
performing image compression. ANN-based compression schemes have outperformed
conventional hand-engineered algorithms for lossy and lossless image
compression. We have designed an ad-hoc ANN-based image compression scheme to
reduce the amount of data needed to be stored and retrieved on space missions
studying solar dynamics. In this work, we propose an attention module to make
use of both local and non-local attention mechanisms in an adversarially
trained neural image compression network. We have also demonstrated the
superior perceptual quality of this neural image compressor. Our proposed
algorithm for compressing images downloaded from the SDO spacecraft performs
better in rate-distortion trade-off than the popular currently-in-use image
compression codecs such as JPEG and JPEG2000. In addition we have shown that
the proposed method outperforms state-of-the art lossy transform coding
compression codec, i.e., BPG.
