Live Demo

The algorithm selects two separators from an alphabet of around 90 ascii characters. The segment characters cannot appear in the uncompressed text. It then looks at every substring, so in the text "hello", it would look at "h", "he", "hel", "hell", "hello", "e", "el"... and so on and adds potentially viable substrings to a register. It calculates each substring's priority based on the number of bytes it would save if added to the dictionary where [ priority = (num matches) * length - (length + (num matches * 2)) ] and puts them in a catalog sorted by priority. It then moves through the the substrings from most effective to least recalculating priorities as it simulates removing the substrings, thus pruning and resorting the catalog. It then looks for conflicts between the letters of the alphabet that will actually be used based on the size of the pruned catalog and resolves any conflicts by adding each conflicting character to the catalog with a high priority so as to achieve 1 byte to 1 byte matching and not impact size of the compressed data. It then creates a hash table ie: the dictionary using the alphabet as a base(alphabet.length) number system. Should the size of the dictionary exceed the size of the alphabet it goes back and fills in the "leading zeros" for consistancy. It then uses regular expressions to apply the hashtable to the data compressing it and then amends the dictionary along with header information to the compressed data.

Decompression works by reading the header information and parsing the dictionary. It then inserts the 1st separator every other character after reading the order of magnitude of compressed data, ie: if each hash is one byte or more. Then using regular expressions it replaces hashes changing surrounding separators of a replacement to the second separator so as not to replace a substring of a substring in following passes, and finally removes all separators revealing the uncompressed data.

The compressed data structure is as follows

compressed data structure

1 byte 																	| separator 1
1 byte																	| separator 2
n bytes separated by 1st separator										| dictionary of keys/values assumed to altenate
1 byte 2nd separator													| end of dictionary
m bytes of 1st separator												| where m is order of magnitude
n bytes of data represented with fixed magnitude of m “leading zeros” 	| compressed data

The data can then be decompressed without pre-agreeing on a dictionary or any other information. This is optimal for sending compressed data to arbitrary recipients, or long term data storage. Because it's smart and prioritizes substrings to create the dictionary before any actual compression is done it typically achieves better compression than algorithms that step through the data and build their dictionary linearly. In high entropy or short data where compression would result in increased size it opts not to compress anything, this again is in contrast to linear algorithms like lzw.

• Ability to manually provide a static or seed dictionary, for cases where pre-determining a dictionary can save additional bytes and allow more customization
• port to Python and PHP
• optimize code and algorithm