The huge improvements in resolution and dynamic range of current  and future CNES remote sensing missions
(from 5m/2.5m in Spot5 to 70cm in Pleiades) illustrate the increasing need of efficient on-board image compressors.
Many techniques have been considered by CNES during the last years in order to go beyond usual compression ratios:
new image transforms or post-transforms , exceptional processing , selective compression .
However, even if significant improvements have been obtained, none of those techniques has ever contested an essential
drawback in current on-board compression schemes: fixed-rate (or compression ratio).
This classical assumption provides highly-predictable data volumes that simplify storage and transmission. But on the
other hand, it demands to compress every image-segment (strip) of the scene within the same amount of data. Therefore,
this fixed bit-rate is dimensioned on the worst case assessments to guarantee the quality requirements in all areas of the
image. This is obviously not the most economical way of achieving the required image quality for every single segment.
Thus, CNES has started a study to re-use existing compressors  in a Fixed-Quality/Variable bit-rate mode. The main
idea is to compute a local complexity metric in order to assign the optimum bit-rate to comply with quality requirements.
Consequently, complex areas are less compressed than simple ones, offering a better image quality for an equivalent
“Near-lossless bit-rate” of image segments has revealed as an efficient image complexity estimator. It links quality
criteria and bit-rates through a single theoretical relationship. Compression parameters are thus automatically computed
in accordance with the quality requirements. In addition, this complexity estimator could be implemented in a one-pass
compression and truncation scheme.