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.

翻译：美国国家航空航天局（NASA）的太阳动力学观测站（SDO）任务每天从其地球同步轨道收集1.4TB数据。SDO数据包括在不同波长下捕获的太阳图像，其主要科学目标是理解控制太阳的动态过程。近年来，端到端优化的人工神经网络（ANN）在图像压缩方面展现出巨大潜力。基于ANN的压缩方案在有损和无损图像压缩中已超越传统人工设计的算法。我们设计了一种针对性的ANN图像压缩方案，以减少研究太阳动力学的太空任务中需要存储和检索的数据量。在这项工作中，我们提出了一种注意力模块，在对抗训练神经图像压缩网络中同时利用局部和非局部注意力机制。我们还展示了该神经图像压缩器卓越的感知质量。我们提出的用于压缩SDO航天器下载图像的算法，在率失真权衡上优于当前广泛使用的图像压缩编解码器（如JPEG和JPEG2000）。此外，我们证明了所提方法优于最先进的有损变换编码压缩编解码器（即BPG）。