agf

Short:        AGF V0.9 - n*8-bit Sample Pre-Packing Processor
Author:       olethros@geocities.com (Christos Dimitrakakis)
Uploader:     olethros geocities com (Christos Dimitrakakis)
Type:         util/pack
Requires:     68020+ (fpu opt.)
Architecture: m68k-amigaos

OVERVIEW

AGF is a sample pre-processor. It transofrms the data into a form having very
little information content. This makes it easier for compression programs to
pack it down to a small size. AGF combined with GZIP gives an average
compression of 50% and it is always better than any other compression method on
its own. It is similar to ADPCM, but better :)

HISTORY

06-09-1999 : Released a version that works :)
05-09-1999 : Released a version that works properly (more or less)

SUMMARY

AGF - Adaptive Gradient-descent FIR filter.

This is a neural-network-like adaptive FIR filter, employing a neural
network of 32 neurons. The adaptation is deterministic, which means
that the sample can be recovered from the processed file without
needing to save an FIR coefficients to it as well. Adaptation is done
on-line, on a sample-by-sample basis.

USAGE

AGF.fpu MODE sample processed_sample
AGF.int MODE sample processed_sample

The processed sample can then be efficiently packed with any kind of packer.
I recommend xpk (xGZIP or xSQSH). lha/lzx will also do :)
The results are always MUCH better.

Modes:
   x  :  extract (decode) using a linear ANN
   c  :  compress (encode) using a linear ANN
   xd :  extract (decode) using a static filter
   cd :  compress (encode) using a static filter

AGF.fpu & AGF.int, implement the same algorithm using floating point and fixed
point representations respectively. The first one is compiled specifically for
68060 with FPU and the second for 68060 (using the math libs for any FPU
instructions..  which are only a couple).  The integer version is twice as fast
on my 68030+68882..  and the packing performance difference is negligible.  I
expect the int version to be also faster on 060 machines (lots of MULs), but
maybe the .fpu version is faster on 040..  test it..


OUTPUT

It outputs the average error of the ANN predictor and when it finishes it shows
the values of the ANN weights.. in case you are interested :)


TODO

Add an RBF layer before the 32-neuron layer.
Make an xpksublib out of it.
Add options for adjusting the number of coefficients and adaptation rate.


BUGS

Bugs Reports to olethros@geocities.com with "AGF BUG" as the subject message please

SEE ALSO

see also dev/basic/gasp.lha for a similar pre-processor where the adaptive
process is controlled by a Genetic Algorithm