  NASA's Solar Dynamics Observatory (SDO) mission collects extensive data to
monitor the Sun's daily activity. In the realm of space mission design, data
compression plays a crucial role in addressing the challenges posed by limited
telemetry rates. The primary objective of data compression is to facilitate
efficient data management and transmission to work within the constrained
bandwidth, thereby ensuring that essential information is captured while
optimizing the utilization of available resources. This paper introduces a
neural video compression technique that achieves a high compression ratio for
the SDO's image data collection. The proposed approach focuses on leveraging
both temporal and spatial redundancies in the data, leading to a more efficient
compression. In this work, we introduce an architecture based on the
Transformer model, which is specifically designed to capture both local and
global information from input images in an effective and efficient manner.
Additionally, our network is equipped with an entropy model that can accurately
model the probability distribution of the latent representations and improves
the speed of the entropy decoding step. The entropy model leverages a
channel-dependent approach and utilizes checkerboard-shaped local and global
spatial contexts. By combining the Transformer-based video compression network
with our entropy model, the proposed compression algorithm demonstrates
superior performance over traditional video codecs like H.264 and H.265, as
confirmed by our experimental results.
