  The volume of remote sensing data is experiencing rapid growth, primarily due
to the plethora of space and air platforms equipped with an array of sensors.
Due to limited hardware and battery constraints the data is transmitted back to
Earth for processing. The large amounts of data along with security concerns
call for new compression and encryption techniques capable of preserving
reconstruction quality while minimizing the transmission cost of this data back
to Earth. This study investigates image compression based on convolutional
variational autoencoders (CVAE), which are capable of substantially reducing
the volume of transmitted data while guaranteeing secure lossy image
reconstruction. CVAEs have been demonstrated to outperform conventional
compression methods such as JPEG2000 by a substantial margin on compression
benchmark datasets. The proposed model draws on the strength of the CVAEs
capability to abstract data into highly insightful latent spaces, and combining
it with the utilization of an entropy bottleneck is capable of finding an
optimal balance between compressibility and reconstruction quality. The balance
is reached by optimizing over a composite loss function that represents the
rate-distortion curve.
