  The evolution of parallel I/O library as well as new concepts such as 'in
transit' and 'in situ' visualization and analysis have been identified as key
technologies to circumvent I/O bottleneck in pre-exascale applications.
Nevertheless, data structure and data format can also be improved for both
reducing I/O volume and improving data interoperability between data producer
and data consumer. In this paper, we propose a very lightweight and
purpose-specific post-processing data model for AMR meshes, called lightAMR.
Based on this data model, we introduce a tree pruning algorithm that removes
data redundancy from a fully threaded AMR octree. In addition, we present two
lossless compression algorithms, one for the AMR grid structure description and
one for AMR double/single precision physical quantity scalar fields. Then we
present performance benchmarks on RAMSES simulation datasets of this new
lightAMR data model and the pruning and compression algorithms. We show that
our pruning algorithm can reduce the total number of cells from RAMSES AMR
datasets by 10-40% without loss of information. Finally, we show that the
RAMSES AMR grid structure can be compacted by ~ 3 orders of magnitude and the
float scalar fields can be compressed by a factor ~ 1.2 for double precision
and ~ 1.3 - 1.5 in single precision with a compression speed of ~ 1 GB/s.
