It is written for embedding in user programs mainly and it is not intended
for immediate use. I was interested in speed and performance improvements
of abstract PPM model [1-6] only, without tuning it to particular data
types, therefore compressor works good enough for texts, but it is not so
good for nonhomogeneous files (executables) and for noisy analog data
(sounds, pictures etc.). Program is very memory consuming, you can choose
balance between execution speed and memory economy, on one hand, and
compression performance, on another hand, with the help of model order
selection option (-o).
OPTIONS
-d <delete file[s] after processing, default: disabled>
-s <silent>
-fName <set output file name to Name>
-mN <use N MB memory - [1,256], default: 10> The PPMII algorithm
might need a lot of memory, especially when used on large files and/or used
with large model order. If PPMd needs more memory than you give it, the
compression will be worse. The exact effect is dependent on the -r option.
-oN <set model order to N - [2,16], default: 4> Bigger model
orders almost surely results in better compression and surely more memory
and CPU usage.
-r{0,1,2} <>
Methods of restoration of model correctness at memory insufficiency:
'-r0 - restart model from scratch'. This method is not optimal for any
type of data sources, but it works fast and efficient in average, so it is
the recommended method (default).
'-r1 - cut off model'. This method is optimal for quasistationary sources
when the period of stationarity is much larger than period between cutoffs.
As a rule, it gives better results, but it is slower than other methods and
it is unstable against fragmentation of memory heap at high model orders and
low memory.
'-r2 - freeze model'. This method is optimal for stationary sources (show
me such source when You will find it ;-)). It is fast and efficient for such
sources.
EXAMPLES
To run this program the standard way type:
PPMd e /tmp/myfile Alternatively you can run it as:
PPMd -e -o 16 /tmp/myfile
AUTHORS
PPMd was written by Dmitry Shkarin <dmitry.shkarin@mtu-net.ru> and
Dmitry Subbotin.
