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IMAGE COMPRESSIONAND DECOMPRESSION
USING ANN
By
Mr.Mahantesh Paramashetti Anusha.GParveen.A.G
Pallavi.S.Yadav
Christeena.S
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CONTENTS
Introduction
Biologically Inspired Neuron
Artificial Neural Networks
Back Propagation AlgorithmCompression Techniques
Implementations
Advantages
Disadvantages
Applications
Conclusion
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INTRODUCTION
Uncompressed multimedia data requiresconsiderable storage capacity and transmissionbandwidth.
Apart from the existing technology like JPEGand MPEG standards, new technology such asneural networks are used for imagecompression.
Natural images are captured using imagesensors and stored in memory banks. Largestorage space is required.
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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 64kbpsand 54mbps transmitting a three hour movierequires in uncompressed format takes 2917
years and 19 days respectively.
Transmission of huge image data is timeconsuming.
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Artificial neural networks has beenchosen for image compression due totheir massively parallel and distributedarchitecture.
The idea behind this Trainingcommands is the Back propagationalgorithm.
The focus of this project is to implementthe Neural Architecture Digitally.
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Biological Neurons
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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 severalprocesses 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.
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Biologically Inspired Neuron
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Artificial Neural Networks
Artificial Neural Networks are used toprocess the information the way
biological systems process analog signals
like image and sound.
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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 orthemselves
Information flow is multidirectional
Sense of time and memory of previous state(s)
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Artificial Neuron System
Input layer
Hidden layer Output layer
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Block Diagram of Neural Architecture
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Back propagation algorithm
Information about errors is filtered back
through the system and it is used to adjust the
connections between the layers, thusimproving performance.
The Feed-Forward Neural Network
architecture is capable of approximating most
problems with high accuracy and
generalization ability.
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The Back propagation algorithm is used to
update weights and bias of the neural
networks.
Weight and bias elements of the neurondecides the functionality of the network.
Value of these weight and bias elements arecalculated during training phase.
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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 modulatethe analog signal that is actually transmitted
through the channel or stored in a storage
medium.
COMPRESSION
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COMPRESSION TECHNIQUES
JPEG
Wavelet
GIF
M-JPEG
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Original imagesOriginal images
Image scaling(256x256)
Adding bias & weights a
Vector values of the scaled Images (16x4096)
Combining these images to increase the resolution (16x32768)
Normalizing the combined image
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normalizing
a
Training the network
testing each image
Comparing scaled &decompressed image
by finding their PSNR & MSE
values
Each image is converted
to vector form
Passing the image
through the network
denormalizing
Each image is converted
to vector form
Passing the image
through the network
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IMPLEMENTATION
MATLAB version R2007b.
The Maximum error, MSE andPSNR values are calculated.
Hardware implementation is
done using FPGA board (Spatan
3 ).
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Neural network training & performance
plots: The neural network is trainedusing the nntraintool, available
in MATLAB.
The plot of MSE wrt epochs fordifferent iterations are as shown:
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Advantages
A neural network can perform tasks that a
linear program cannot.
When an element of the neural network fails, itcan 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.
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Disadvantages
The neural network needs training to
operate.
The architecture of a neural network is
different from the architecture ofmicroprocessors therefore needs to be
emulated.
Requires high processing time for largeneural networks.
As the number of neurons increases the
network becomes complex.
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Applications
Pattern Matching
Pattern RecognitionOptimizationVector Quantization
Data Clustering
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CONCLUSIONChipscope 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.
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THANK YOU