This document discusses using artificial neural networks for image compression and decompression. It begins with an introduction explaining the need for image compression due to large file sizes. It then describes biologically inspired neurons and artificial neural networks. The document outlines the backpropagation algorithm, various compression techniques, and how neural networks were implemented in MATLAB and on an FPGA board for this project. It discusses the advantages of neural networks for this application, some disadvantages, and examples of applications. In conclusion, it states that the design was successfully implemented on an FPGA board and input and output values were similar, showing the neural network approach works for image compression.

1. IMAGE COMPRESSION AND
DECOMPRESSION USING ANN
Guided by
Mr.Mahantesh Paramashetti
By
Anusha.G
Parveen.A.G
Pallavi.S.Yadav
Christeena.S

2. CONTENTS
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Introduction
Biologically Inspired Neuron
Artificial Neural Networks
Back Propagation Algorithm
Compression Techniques
Implementations
Advantages
Disadvantages
Applications
Conclusion

3. INTRODUCTION
 Uncompressed
multimedia data requires
considerable storage capacity and transmission
bandwidth.
Apart from the existing technology like JPEG
and MPEG standards, new technology such as
neural networks are used for image
compression.
Natural images are captured using image
sensors and stored in memory banks. Large
storage space is required.

4. eg: A color image of size 256x256 requires a
storage space of 1.5 Mega bits.
 Storage cost for 1 GB is approximately Rs.
200.
With the available bandwidth of 64kbps and
54mbps transmitting a three hour movie
requires in uncompressed format takes 2917
years and 19 days respectively.
Transmission of huge image data is time
consuming.

5. Artificial neural networks has been chosen for
image compression due to their massively parallel
and distributed architecture.
The idea behind this Training commands is the
Back propagation algorithm.
The focus of this project is to implement the
Neural Architecture Digitally.

6. Biological Neurons

7. The Analogy to the Brain
 Neurons are basic signaling units of the nervous
system of a living being in which each neuron is
a discrete cell whose several processes are
from its cell body.
 The basic element of human brain has abilities
to remember, think and apply previous
experiences to our every action.
 Neural networks process information in a
similar way the human brain does.

8. Biologically Inspired Neuron

9. Artificial Neural Networks
 Artificial Neural Networks are used to process
the information the way biological systems
process analog signals like image and sound.

10. Types of ANN
Feed forward networks
Information only flows one way
One input pattern produces one output
No sense of time (or memory of previous state)
Recurrency
Nodes connect back to other nodes or
themselves
Information flow is multidirectional
Sense of time and memory of previous state(s)

11. Artificial Neuron System
• Input layer
• Hidden layer
• Output layer
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12. Block Diagram of Neural
Architecture

13. Back propagation algorithm
 Information about errors is filtered back
through the system and it is used to adjust the
connections between the layers, thus
improving performance.
 The Feed-Forward Neural Network architecture
is capable of approximating most problems
with high accuracy and generalization ability.

14.  The Back propagation algorithm is used to
update weights and bias of the neural
networks.
 Weight and bias elements of the neuron
decides the functionality of the network.
 Value of these weight and bias elements are
calculated during training phase.

15. COMPRESSION
 Image compression refers to the task of
reducing the amount of data required to store
or transmit an image.
 The compressed image is then subjected to
further digital processing such as error control
coding, encryption or multiplexing with other
data sources, before being used to modulate
the analog signal that is actually transmitted
through the channel or stored in a storage
medium.

16. COMPRESSION TECHNIQUES
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JPEG
Wavelet
GIF
M-JPEG

17. Original images
Image scaling(256x256)
Vector values of the scaled Images (16x4096)
Combining these images to increase the resolution (16x32768)
Normalizing the combined image
Adding bias & weights
Bukarica Leto, bleto@rcub.bg.ac.rs
a
17

18. a
Training the network
testing each image
Comparing scaled &
decompressed image
by finding their PSNR &
MSE values
Each image is converted
to vector form
normalizing
Passing the image
through the network
denormalizing

19. IMPLEMENTATION
 MATLAB version R2007b.
 The Maximum error, MSE and PSNR values are
calculated.
 Hardware implementation is done using FPGA
board (Spatan 3 ).

20. Neural network training &
performance plots:
The neural network is trained
using the nntraintool, available
in MATLAB.
The plot of MSE wrt epochs for
different iterations are as shown:
Bukarica Leto, bleto@rcub.bg.ac.rs
20

21. Advantages
• A neural network can perform tasks that a linear
program cannot.
• When an element of the neural network fails, it
can continue without any problem by their
parallel nature.
• A neural network learns and does not need to
be reprogrammed.
• It works even in the presence of noise with good
quality output.

22. Disadvantages
 The neural network needs training to operate.
 The architecture of a neural network is
different from the architecture of
microprocessors therefore needs to be
emulated.
 Requires high processing time for large neural
networks.
 As the number of neurons increases the
network becomes complex.

23. Applications
 Pattern Matching
 Pattern Recognition
 Optimization
 Vector Quantization
 Data Clustering

24. CONCLUSION
• Chipscope Pro Analyzer can easily implement
the design on FPGA kit.
• The analysis showed that comparision
between input and output values was proved
to be similar.
• Using Chipscope Pro Analyzer smaller
architectures can be easily built.

25. THANK YOU