This document discusses Huffman coding for image compression. It explains that image compression reduces data storage requirements by decreasing redundancies in image representation without visually reducing image quality. Huffman coding builds a tree by combining the two bins with the lowest histogram values until one bin remains, encodes the tree, and then encodes the residual image. Examples are provided to illustrate the Huffman coding algorithm. The summary notes that Huffman coding achieves minimal redundancy by sorting symbols by probability and assigning codewords at each node of the binary tree.

1. Huffman coding
By
Md. Fazle Rabbi
16CSE057

2. 4.2
Introduction
• Image compression is a method to reduce the redundancies in
image representation in order to decrease data storage
requirements
• It is a technique used to compress an image without visually
reducing the quality of the image itself.
• The goal of these processes is to represent an image with the
same quality level, but in a more solid form.

3. 4.3
Why the image must be compressed?
• The large storage requirement of multimedia data.
• The video or image files consume large amount of data and it
always required very high bandwidth networks in transmission
as well as communication costs.

4. 4.4
Huffman Coding Algorithm for Image Compression
• Build a Huffman tree by sorting the histogram and
successively combine the two bins of the lowest value
until only one bin remains.
• Encode the Huffman tree and save the Huffman tree
with the coded value.
• Encode the residual image.

5. 4.5
Huffman Coding

6. 4.6
Example I

7. 4.7
Example I- Cont..

8. 4.8
Example I- Cont..

9. 4.9
Example II

10. 4.10
Example II

11. 4.11
Example II

12. 4.12
Example II

13. 4.13
Summary of Huffman Code
 Achieve minimal redundancy subject to the constraint that the source
symbols be coded one at a time.
 Sorting symbols in descending probabilities is the key in the step of
source reduction.
 The codeword assignment is not unique. Exchange the labeling of
“0” and “1” at any node of binary codeword tree would produce
another solution that equally works well.
 Only works for a source with finite number of symbols (otherwise, it
does not know where to start).

14. 4.14
Thank You