Huffman coding01

Huffman Coding
The idea behind Huffman coding is to find a way to compress
the storage of data using variable length codes. Our standard
model of storing data uses fixed length codes. For example,
each character in a text file is stored using 8 bits. There are
certain advantages to this system. When reading a file, we
know to ALWAYS read 8 bits at a time to read a single
character. But as you might imagine, this coding scheme is
inefficient. The reason for this is that some characters are
more frequently used than other characters. Let's say that the
character 'e' is used 10 times more frequently than the
character 'q'. It would then be advantageous for us to use a 7
bit code for e and a 9 bit code for q instead because that could
shorten our overall message length.
Huffman coding finds the optimal way to take advantage of
varying character frequencies in a particular file. On average,
using Huffman coding on standard files can shrink them
anywhere from 10% to 30% depending to the character
distribution. (The more skewed the distribution, the better
Huffman coding will do.)
The idea behind the coding is to give less frequent characters
and groups of characters longer codes. Also, the coding is
constructed in such a way that no two constructed codes are
prefixes of each other. This property about the code is crucial
with respect to easily deciphering the code.

Building a Huffman Tree
The easiest way to see how this algorithm works is to work
through an example. Let's assume that after scanning a file we
find the following character frequencies:
Character Frequency
'a' 12
'b' 2
'c' 7
'd' 13
'e' 14
'f' 85
Now, create a binary tree for each character that also stores
the frequency with which it occurs.
The algorithm is as follows: Find the two binary trees in the list
that store minimum frequencies at their nodes. Connect these
two nodes at a newly created common node that will store NO
character but will store the sum of the frequencies of all the
nodes connected below it. So our picture looks like follows:
9 12 'a' 13 'd' 14 'e' 85 'f'
/
2 'b' 7 'c'

Now, repeat this process until only one tree is left:
21
/
9 12 'a' 13 'd' 14 'e' 85 'f'
/
2 'b' 7 'c'
21 27
/ /
9 12 'a' 13 'd' 14 'e' 85 'f'
/
2 'b' 7 'c'
48
/
21 27
/ /
9 12 'a' 13 'd' 14 'e' 85 'f'
/
2 'b' 7 'c'
133
/
48 85 'f'
/
21 27
/ /
9 12 'a' 13 'd' 14 'e'
/
2 'b' 7 'c'
Once the tree is built, each leaf node corresponds to a letter
with a code. To determine the code for a particular node, walk

a standard search path from the root to the leaf node in
question. For each step to the left, append a 0 to the code and
for each step right append a 1. Thus for the tree above we get
the following codes:
Letter Code
'a' 001
'b' 0000
'c' 0001
'd' 010
'e' 011
'f' 1
Why are we guaranteed that one code is NOT the prefix of
another?
Find a set of valid Huffman codes for a file with the given
character frequencies:
Character Frequency
'a' 15
'b' 7
'c' 5
'd' 23
'e' 17
'f' 19

Calculating Bits Saved
All we need to do for this calculation is figure out how many
bits are originally used to store the data and subtract from that
how many bits are used to store the data using the Huffman
code.
In the first example given, since we have six characters, let's
assume each is stored with a three bit code. Since there are 133
such characters, the total number of bits used is 3*133 = 399.
Now, using the Huffman coding frequencies we can calculate
the new total number of bits used:
Letter Code Frequency Total Bits
'a' 001 12 36
'b' 0000 2 8
'c' 0001 7 28
'd' 010 13 39
'e' 011 14 42
'f' 1 85 85
Total 238
Thus, we saved 399 - 238 = 161 bits, or nearly 40% storage
space. Of course there is a small detail we haven't taken into
account here. What is that?

Huffman Coding is an Optimal Prefix Code
Of all prefix codes for a file, Huffman coding produces an
optimal one. In all of our examples from class on Monday, we
found that Huffman coding saved us a fair percentage of
storage space. But, we can show that no other prefix code can
do better than Huffman coding.
First, we will show the following:
Let x and y be two characters with the least frequencies in a
file. Then there exists an optimal prefix code for C in which the
codewords for x and y have the same length and differ only in
the last bit.
Here is how we will prove this:
Assume that a tree T stores an optimal prefix code. Let and
characters a and b be sibling nodes stored at the maximum
depth of the tree. We will show that we can create T' with x
and y as siblings at the lowest depth of the tree such that the
number of bits used for the coding with T' is the same as with
T. (Let f(a) denote the frequency of the character a. Without
loss of generality, assume f(x) ≤ f(y) and f(a) ≤ f(b). It also
follows that f(x) ≤ f(a) and f(y) ≤ f(b). Let h be the height of the
tree T. Let x have a depth of dx in T and y have a depth of dx in
T.)
Create T' as follows: swap the nodes storing a and x, and then
swap the nodes storing b and y. Now, we have that the depth of
x and y in T' is h, the depth of a is dx and the depth of b is dy in
T'.

Now, let's calculate the change in the number of bits used for
the coding with tree T' with the coding in tree T. (Note: Since
all other codes remain unchanged, we only need to analyze the
total number of bits it takes to code a, b, x and y.)
# bits for tree T (for a,b,x and y) = hf(a) + hf(b) + dxf(x) dyf(y)
# bits for tree T' (for a, b, x, and y) = dxf(a) + dyf(b) + hf(x) +
hf(y).
Difference =
hf(a) + hf(b) + dxf(x) dyf(y) - (dxf(a) + dyf(b) + hf(x) + hf(y)) =
hf(a) + hf(b) + dxf(x) dyf(y) - dxf(a) - dyf*b) - hf(x) - hf(y) =
h(f(a) - f(x)) + h(f(b)-f(y)) + dx(f(x) - f(a)) + dy(f(y) - f(b)) =
h(f(a) - f(x)) + h(f(b)-f(y)) - dx(f(a) - f(x)) - dy(f(b) - f(y)) =
(h - dx)(f(a) - f(x)) + (h - dy)(f(b) - f(y))
Notice that all four of the terms above must be non-negative
since we know that h ≥ dx, h ≥ dy, f(a) ≥ f(x), and f(b) ≥ f(y).
Thus, it follows that this difference must be 0. Thus, the
number of bits to used in a code where x and y (the two
characters with lowest frequency) are siblings at maximum
depth of the coding tree is optimal.
In layman's terms, give me what you think is an optimal coding
tree, and I can create a new one from it with the two nodes
corresponding to low frequencies at the bottom of the tree.
To complete the proof, you'll notice that by construction,
Huffman coding ALWAYS makes sure that the nodes with the

lowest frequencies are at the bottom of the coding tree, all the
way through the construction. (You can't find any pair of
nodes for which this isn't true.) Technically, to carry out the
proof, you'd use induction, but we'll skip that for now...

Huffman coding01

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Huffman coding01