Implementation of Image and Audio Compression Techniques Using

7/29/2019 Implementation of Image and Audio Compression Techniques Using

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PROJECT GUIDE :V.V.RAMAKRISHNA M.Tech, M.Hemanth kumarM.NagakumarN.Nagasaidireddy


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CONTENTS:

OBJECTIVE

INTRODUCTION

WORKING PRINCIPLE

HOW DCT CAN BE USED FOR COMPRESSION

IMPLEMENTATION

IMAGE COMPRESSION

AUDIO COMPRESSION

MATLAB FUNCTIONS USED IN PROGRAMS

CONCLUSION


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OBJECTIVE:

TO IMPLEMENT GENERAL IMAGE COMPRESSION,

STANDARD JPEG COMPRESSION AND STANDARD

MP3 COMPRESSION TECHNIQUES USING DCT AND TO

ANALYSE THE RESULTS.


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INTRODUCTION:

Data compression is the technique to reduce the redundancies indata representation in order to decrease data storage requirements

and hence communication costs. The data may be image(jpeg),

audio(mp3) or video(mpeg).

Reducing the storage requirement is equivalent to increasing thecapacity of the storage medium and hence communication

bandwidth.

Thus the development of efficient compression techniques willcontinue to be a design challenge for future communication systems

and advanced multimedia applications.


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The existing techniques used for compressing image files are

broadly classified into two categories, namely lossless and

lossy compression techniques.

In lossless compression, image data is reduced while image

information is totally preserved.

First step is , it uses the predictive encoding which uses thegray level of each pixel to predict the gray value of its right

neighbor and these values with very small deviation are stored.

Statistical encoding is another step in lossless data reduction.

Statistical encoding is used code to gray level statistics of the

images based on predictive coding.


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Lossless compression algorithms used in text compression

and medical imaging applications etc.

In lossy compression techniques, the original digital image is

usually transformed through an invertible linear transform into

another domain, where information and redundancy are highly

de-correlated by the transform.

This de-correlation concentrates the important image

information into a more compact form.

The transformed coefficients are then quantized yielding bit-

streams containing long stretches of zeros.


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Such bit-streams can be coded efficiently to remove the

redundancy and store it into a compressed file.

The decompression reverses this process to produce the

recovered image.

The advantage of lossy methods over lossless methods is that insome cases a lossy method can produce a much smaller

compressed file than any known lossless method, while still

meeting the requirements of the application.

Lossy methods are most often used for compressing sound,

images or videos and lossless compression is used for text

compression and medical image compression.


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WORKING PRINCIPLE:

Data is represented as a combination of information and

redundancy.

Information is the portion of data that must be preserved

permanently in its original form in order to correctly interpret the

meaning or purpose of the data.

Redundancy is that portion of data that can be removed when it is

not needed and can be reinserted to interpret the data when needed.

The redundancy in data representation is reduced such a way that

it can be subsequently reinserted to recover the original data, which

is called decompression of the data.


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Data compression can be understood as a method that takes

an input data D and generates a shorter representation of the

data c(D) with less number of bits compared to that of D.

The reverse process is called decompression, which takes the

compressed data c(D) and generates or reconstructs the data D.

Sometimes the compression (coding) and decompression

(decoding) systems together are called a CODEC .


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ENCODER

DIVIDE SOURCEINTO N x N

BLOCKS

2-DDCT

TRANSFORM

QUANTIZATION

BLOCK DIAGRAM:

SOURCE IMAGE

OR

AUDIO

COMPRESSEDIMAGE

OR AUDIO


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Why only DCT:

Properties of DCT

Decorrelation :

The main advantage of signal transformation is the removal of

redundancy between neighboring values.

This leads to uncorrelated transform coefficients whichcan be

encoded independently.

Energy Compaction

Efficiency of a transformation scheme can be directly gauged by

its ability to pack input data into as few coefficients as possible.


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This allows the quantizer to discard coefficients with

relatively small amplitudes without introducing visual

distortion in the reconstructed image.

DCT exhibits excellent energy compaction for highly

correlated signals.

So it is most oftenly used for compression image , audio

and video files in multimedia applications.


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The basic operation of the DCT is as follows:

The input image is N by M;

f(i,j) is the intensity of the pixel in row i and column j;

F(u,v) is the DCT coefficient in row k1 and column k2 of the

DCT matrix.

For most images, much of the signal energy lies at low

frequencies; these appear in the upper left corner of the DCT.

Compression is achieved since the lower right valuesrepresent higher frequencies, and are often small - small enough

to be neglected with little visible distortion.


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1D Forward DCT Given a list ofnintensity values I(x),

where x = 0, , n-1

Compute the nDCT coefficients:

1...0,2

)12(cos)()(2)(1

0

nun

xxIuCn

uFn

x

otherwise

uforuCwhere

1

,02

1

)(

CS 414 - Spring 2012


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Equations for 2D DCT Forward DCT:

Inverse DCT:

m

vy

n

uxyxIvCuC

nmvuF

m

y

n

x 2

)12(cos*

2

)12(cos*),()()(

2),(

1

0

1

0

mvy

nuxvCuCuvF

nmxyI

m

v

n

u 2)12(cos*

2)12(cos)()(),(2),(

1

0

1

0


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IMPORTANCE OF CO-EFFICIENTS:

F(0,0)

includes the lowest frequency in both directions

is called DC coefficient

Determines fundamental color of the block

F(0,1) . F(7,7)

are called AC coefficients

Their frequency is non-zero in one or both directions


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JPEG Compression (Baseline)

FDCT

Source

Image

Quantizer

Entropy

Encoder

TableTable

Compressed

image data

DCT-based encoding

8x8 blocks

R

B

G

CS 414 - Spring 2012

Image Preparation

Image Processing


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JPEG Process:

Original image is divided into blocks of 8 x 8.

Pixel values of a black and white image range from 0-255 but DCT

is designed to work on pixel values ranging from -128 to

127.Therefore each block is modified to work in the range.

Equation(1) is used to calculate DCT matrix.

DCT is applied to each block by multiplying the modified block

with DCT matrix on the left and transpose of DCT matrix on its right.

Each block is then compressed through quantization.

Quantized matrix is then entropy encoded using run length coding.


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Audio Compression Audio signal overview

Sampling rate (# of samples per second)

Bit rate (# of bits per second). Typically,

uncompressed stereo 16-bit 44.1KHz signal hasa 1.4MBps bit rate.

Number of channels (mono / stereo /multichannel)

Reduction by lowering those values or bydata compression / encoding


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The MP3 COMPRESSION Algorithm:

Converting the original audio into 8192 x 8192

Applying 2-D DCT to the above image.

Reconstructing the required number of samples.

For compression factor of n , we will reconstruct (1/n) of

the total number of samples.

Plotting or playing the audio signals to interpret the

results.

Saving the compressed audio files to verify the

compression factor.


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Function of matlab used in programming

Wavread():

Used to read an audio file from the specified location.

Wavwrite()

Used to write an audio file from the specified location.

Wavplay()

Used to play an audio file from the specified location.

Specgram()

Used to plot spectrum a given signal.


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Dct2():

Used to calculate the 2-D DCT coefficients of the

given input.

Idct2():

Used to calculate the 2-D IDCT coefficients of the

given input.

Imread():

Used to read the image from the specified location.

Imshow():

Used to show the image on the desktop from the

specified location.


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Imcrop():

Used to crop the image to the specified size.

Rgb2gray():

Used to convert the RGB image into grayscale image.

Imresize():

Used to change the size of an image to the specified

size .


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CONCLUSION:

High compression ratio and better image quality

accomplished which is better than existing methods.

This project has concentrated on development of

efficient and effective algorithm for still image

compression.

Results show that reduction in encoding time with little

degradation in image quality compare to subsisting

method.

Compression ratio is also increased, while comparing

the proposed method with other methods.


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Implementation of Image and Audio Compression Techniques Using