NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLaWin), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off.

为了减少太空任务中的数据存储和视频带宽需求，NASA的太阳动力学观测卫星（SDO）任务收集了大量太阳日常活动的数据。本文提出了一种专为SDO图像设计的新型基于神经Transformer的视频压缩方法，旨在有效利用太阳图像中的时间和空间冗余，以获得高压缩比。实验结果表明，FLaWin Transformer模块对压缩性能有着显著贡献，在速率失真权衡方面胜过传统的手工设计的视频编解码器如H.264和H.265。

太阳动力学观测卫星上的上下文感知神经视频压缩