programming-for-dummies

436
Lossless Data Compression Algorithms
Lossless Data Compression Algorithms
The basic idea behind lossless data compression is to find a way to pack data
in a smaller space more efficiently without losing any of the data in the process.
To do this, lossless data compression algorithms are typically optimized for
specific data, such as text, audio, or video, although the general principles
remain the same no matter what type of data the algorithm is compressing.
Run-length encoding
The simplest lossless data compression algorithm is run-length encoding (RLE).
Basically, this method looks for redundancy and replaces any redundant data
with a much shorter code instead. Suppose you had the following 17-character
string:
WWWBBWWWWBBBBWWWW
RLE looks for redundant data and condenses it into a 10-character string,
like this:
3W2B4W4B4W
The number in front of each letter identifies how many characters the code
replaced, so in this example, the first two characters, 3W represents WWW,
2B represents BB, 4W represents WWWW, and so on.
Run-length encoding is used by fax machines because most images consist
of mainly white space with occasional black areas that represent letters or
drawings.
The Burrows-Wheeler transform algorithm
One problem with run-length encoding is that it works best when repetitive
characters appear grouped together. With a string, like WBWBWB#, RLE can’t
compress anything because no groups of W and B characters are bunched
together. (However, a smart version of the RLE algorithm notices the twocharacter
repetitive string WB and encodes the string as 3(WB)#, which would
tell the computer to repeat the two-character pattern of WB three times.)
When redundant characters appear scattered, run-length encoding can be
made more efficient by first transforming the data to group identical characters
together and then use run-length encoding to compress the data. That’s
the idea behind the Burrows-Wheeler transform (BWT) algorithm, developed
by Michael Burrows and David Wheeler.