Huffman Compression

         Written for the PC-GPE and the World by Joe Koss (Shades)
                       Contact me on irc in #coders


Huffman Compression, also known as Huffman Encoding, is one of many
compression techniques in use today. Others are LZW, Arithmetic Encoding, RLE
and many many more. One of the main benefits of Huffman Compression is how
easy it is to understand and impliment yet still gets a decent compression
ratio on average files.

Many thanks to Al Stevens for his Feb. 1991 article in Dr. Dobb's Programmers
Technical Journal which helped me greatly in understanding Huffman Compression.

(Al Stevens's C source code for Huffman Compression is available from DDJ and
 can also be found on various FTP sites with DDJ source collections.)


The Huffman compression algorythm assumes data files consist of some byte
values that occur more frequently than other byte values in the same file.
This is very true for text files and most raw gfx images, as well as EXE and
COM file code segments.

By analyzing, the algorythm builds a "Frequency Table" for each byte value
within a file. With the frequency table the algorythm can then build the
"Huffman Tree" from the frequency table. The purpose of the tree is to
associate each byte value with a bit string of variable length. The more
frequently used characters get shorter bit strings, while the less frequent
characters get longer bit strings. Thusly the data file may be compressed.

To compress the file, the Huffman algorythm reads the file a second time,
converting each byte value into the bit string assigned to it by the Huffman
Tree and then writing the bit string to a new file.

The decompression routine reverses the process by reading in the stored
frequency table (presumably stored in the compressed file as a header) that
was used in compressing the file. With the frequency table the decompressor
can then re-build the Huffman Tree, and from that, extrapolate all the bit
strings stored in the compressed file to their original byte value form.

The Frequency Table

The Huffman algorythms first job is to convert a data file into a frequency
table. As an example, our data file might contain the text (exluding the
quotation marks): "this is a test"

The frequency table will tell you the frequency of each character in the file,
in this case the frequency table will look like this:

                          Character | Frequency
                              t     |     3 times
                              s     |     3  ..
                           <space>  |     3  ..
                              i     |     2  ..
                              e     |     1  ..
                              h     |     1  ..
                              a     |     1  ..

The Huffman Tree

The huffman algorythm then builds the Huffman Tree using the frequency table.
The tree structure containes nodes, each of which contains a character, its
frequency, a pointer to a parent node, and pointers to the left and right child
nodes. The tree can contain entries for all 256 possible characters and all 255
possible parent nodes. At first there are no parent nodes. The tree grows by
making successive passes through the existing nodes. Each pass searches for two
nodes *that have not grown a parent node* and that have the two lowest
frequency counts. When the algorythm finds those two nodes, it allocates a new
node, assigns it as the parrent of the two nodes, and gives the new node a
frequency count that is the sum of the two child nodes. The next iterations
ignores those two child nodes but includes the new parent node. The passes
continue until only one node with no parent remains. That node will be the root
node of the tree. The tree for our example text will look like this:

   t ----[3]---------------------\
   s ----[3]-\                   |
              -[6]--------------- ------\
--[3]-/                   |       -[14]--
                                 \      /
   i ----[2]---------------\      -[8]-/
   e ----[1]--------\      /
   h ----[1]-\      /
   a ----[1]-/


Compression then involves traversing the tree beginning at the leaf node for
the character to be compressed and navigating to the root. This navigation
iteratively selects the parent of the current node and sees whether the
current node is the "right" or "left" child of the parent, thus determining
if the next bit is a one (1) or a zero (0). Because you are proceeding from
leaf to root, you are collecting bits in the *reverse* order in which you will
write them to the compressed file.

The assignment of the 1 bit to the left branch and the 0 bit to the right is
arbitrary. Also, the actual tree will almost never look quite like the one
in my example. Here is the tree with 1's and 0's assigned to each branch:

   t ----------------------[0]-\
   s ----[0]-\                 |
              ----------------- -[0]---\
--[1]-/                 |        --root
   i ----------------[0]---\      /
   e ----------[0]--\      /         <- writen this direction to compressed file.
   h ----[0]-\      /
   a ----[1]-/

The tree in my example would compress "this is a test" into the bit stream:

   T     H      I     S         I     S          A           T     E     S    T
| 10 | 11110 | 110 | 00 | 01 | 110 | 00 | 01 | 11111 | 01 | 10 | 1110 | 00 | 10 |
  |            ||               ||                ||              ||Partial|
  \    Byte1   /\     Byte2     /\      Byte3     /\     Byte4    /\ Byte5 /
   ------------  ---------------  ----------------  --------------  -------
     10111101       10000111          00001111         11011011     100010..


Decompression involves re-building the Huffman tree from a stored frequency
table (again, presumable in the header of the compressed file), and converting
its bit streams into characters. You read the file a bit at a time. Beginning
at the root node in the Huffman Tree and depending on the value of the bit,
you take the right or left branch of the tree and then return to read another
bit. When the node you select is a leaf (it has no right and left child nodes)
you write its character value to the decompressed file and go back to the root
node for the next bit.

Final words

If you still dont understand Huffman Compression, no amount of source code
is going to help you (one reason I didn't include any, not even psuedo code
helps _understand_ Huffman). Re-read this file again a few times, it will
all seem obvious soon enough (or else you shouldn't be attempting to write
your own compression routines).

Many thanks to Mark Feldman (Myndale) for the first real attempt at a
-collection- of free programmers information that includes both sourcecode
and/or DETAILED documention when necessary.

(IMHO: detailed docs are volumes better than sourcecode .. if you don't
understand it, your just bloody rippin' code!)

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Date this article was posted to 7/16/1999
(Note that this date does not necessarily correspond to the date the article was written)

See Also:
Compression Algorithms

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