An artificial neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships.

1. Topic : Image Compression Using Neural Network
Submitted By :-
Omkar Lokhande (A-68)

2. Content
• Introduction to the Neural Network
• Neural Network Structure
• Neural Network Structure
• Activation Function
• Functions of Neural Network
• Image Compression using BP Neural Network
• Output of this Compression Algorithm
• Other Neural Network Techniques
• References

3. Introduction to the Neural Network
• An artificial neural network is a powerful data
modeling tool that is able to capture and
represent complex input/output relationships.
• Can perform "intelligent" tasks similar to those
performed by the human brain.

4. Neural Network Structure
• A neural network is an interconnected
group of neurons
A Simple Neural Network

5. Neural Network Structure
An Artificial Neuron

6. Activation Function
Depending upon the problem variety of
Activation function is used:
Linear Activation function like step function
Nonlinear Activation function like sigmoid
function

7. Functions of Neural Network
• Compute a known function
• Approximate an unknown function
• Pattern Recognition
• Signal Processing
• Learn to do any of the above

8. Image Compression using BP Neural
Network [1]
• Future of Image Coding(analogous to our visual
system)
• Narrow Channel
• K-L transform
• The entropy coding
of the state vector
hi’s at the hidden layer.

9. Image Compression [2]
• A set of image samples is used to train the
network.
• This is equivalent to compressing the input into
the narrow channel and then reconstructing the
input from the hidden layer.

10. Image Compression [3]
• Transform coding with multilayer Neural
Network: The image to be subdivided into non-
overlapping blocks of n x n pixels each. Such
block represents N-dimensional vector x, N = n x
n, in N-dimensional space. Transformation
process maps this set of vectors into
y=W (input)
output=W-1y

11. Image Compression [4]
The inverse transformation need to reconstruct
original image with minimum of distortions.

12. Output of this Compression
Algorithm

13. Other Neural Network
Techniques
• Hierarchical back-propagation neural network
• Predictive Coding
• Depending upon weight function we have
• Hebbian learning-based image compression
Wi (t + 1)= {W(t) + αhi(t)X(t)}/||Wi (t) + αhi(t)X(t)||

14. References
• Neural networks Wikipedia
(http://en.wikipedia.org/wiki/Neural_network)
• Ivan Vilovic' : An Experience in Image Compression Using
Neural Networks
• Robert D. Dony, Simon Haykin: Neural Network Approaches
to Image Compression
• Constantino Carlos Reyes-Aldasoro, Ana Laura Aldeco: Image
Segmentation and compression using Neural Networks
• Image compression with neural networks - A survey --J.
Jiang*

15. Thank You !