  Compression of hyperspectral images onboard of spacecrafts is a tradeoff
between the limited computational resources and the ever-growing spatial and
spectral resolution of the optical instruments. As such, it requires
low-complexity algorithms with good rate-distortion performance and high
throughput. In recent years, the Consultative Committee for Space Data Systems
(CCSDS) has focused on lossless and near-lossless compression approaches based
on predictive coding, resulting in the recently published CCSDS 123.0-B-2
recommended standard. While the in-loop reconstruction of quantized prediction
residuals provides excellent rate-distortion performance for the near-lossless
operating mode, it significantly constrains the achievable throughput due to
data dependencies. In this paper, we study the performance of a faster method
based on prequantization of the image followed by a lossless predictive
compressor. While this is well known to be suboptimal, one can exploit powerful
signal models to reconstruct the image at the ground segment, recovering part
of the suboptimality. In particular, we show that convolutional neural networks
can be used for this task and that they can recover the whole SNR drop incurred
at a bitrate of 2 bits per pixel.
