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Huffman Compression Codes

by Joe Koss (Shades)


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 agorithm 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 agorithm builds a "Frequency Table" for each byte value within a file. With the frequency table the agorithm 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 agorithm 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 agorithms 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  ..
                                    |     3  ..
                              i     |     2  ..
                              e     |     1  ..
                              h     |     1  ..
                              a     |     1  ..

The Huffman Tree

The huffman agorithm 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 agorithm 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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