  The Herschel Space Observatory of ESA was launched in May 2009 and is in
operation since. From its distant orbit around L2 it needs to transmit a huge
quantity of information through a very limited bandwidth. This is especially
true for the PACS imaging camera which needs to compress its data far more than
what can be achieved with lossless compression. This is currently solved by
including lossy averaging and rounding steps on board. Recently, a new theory
called compressed-sensing emerged from the statistics community. This theory
makes use of the sparsity of natural (or astrophysical) images to optimize the
acquisition scheme of the data needed to estimate those images. Thus, it can
lead to high compression factors.
  A previous article by Bobin et al. (2008) showed how the new theory could be
applied to simulated Herschel/PACS data to solve the compression requirement of
the instrument. In this article, we show that compressed-sensing theory can
indeed be successfully applied to actual Herschel/PACS data and give
significant improvements over the standard pipeline. In order to fully use the
redundancy present in the data, we perform full sky map estimation and
decompression at the same time, which cannot be done in most other compression
methods. We also demonstrate that the various artifacts affecting the data
(pink noise, glitches, whose behavior is a priori not well compatible with
compressed-sensing) can be handled as well in this new framework. Finally, we
make a comparison between the methods from the compressed-sensing scheme and
data acquired with the standard compression scheme. We discuss improvements
that can be made on ground for the creation of sky maps from the data.
