The compression algorithm transforms elements selected from some emsamble of
data-sets (texts, images, ..).
It works well if there is match between the ensemble and transformation.
Here, we have a description of transformation - and a question : "On which
data sets it will work well?" - sort of the Inverse Problem to the usual
approach (which takes ensambles from applications and looks for 'suitable
The algorithm is described intuitively,
by geometrical transformation of 2D images stacked to form 3D structure.
If we do the slicing 'cleverly' and 'data are nice' we get a good compression.
Problem of implementing that programmer is really not told what is a clever,
what geometrical manipulation is good for what ensembles. It restates the
concept of encoding and gives only an example of transformation for one ensemble.
I would suggest to describe general algorithm in pseudocode
SEARCH TERMS: what is pseudocode
so that programmer knows what 'clever' and 'nice' mean (for a given
Here a few references, overviews, and a textbook, showing that that this sketched
idea is among the main techniques used in intros. It may be
original, however full
literature search was not done. That, as well as coding (be it in c
high level matrix language, such as Octave, would IMHO far exceed
the offered price.
Introduction / Lossless Data Compression - basic terms and methods
DATA COMPRESSION STATISTICS AND IMPLICATIONS
Many lossless data compression algorithms exist. Some of the main
techniques in use are the Huffman , Arithmetic , Lempel-Ziv ,
runlength, predictive coding or variations and combinations of these.
Each of these methods can be found in most data compression texts ..
The Design and Analysis of Efficient Lossless Data Compression
Systems. PhD thesis, Department of Computer Science, Brown University,
By David Salomon. Published by Springer (2000). ISBN 0-387-95045-1.
LCCN QA76.9.D33 S25 2000. xvi + 823 page