Lossy and lossless compression using combinational...

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)

Volume 4 Issue 8, August 2015

ISSN: 2278 – 1323 All Rights Reserved © 2015 IJARCET 3419

Lossy and lossless compression using

combinational methods

Ms. C.S Sree Thayanandeswari,M.E, MISTE,

Assistant Professor, Department of ECE, PET

Engineering College, Vallioor.

J Jeya Christy Bindhu Sheeba, Dept of ECE,

IIndM.E(C.S) PET Engineering College,

Vallioor .

Abstract—Image compression is the process of reducing

the amount of data required to represent an image.

Image Compression is used in the field of Broadcast

TV, Remote sensing, Medical Images. Many common

file formats are surveyed and the experimental results

of various states of lossy and lossless compression

algorithms are given .In the proposed method, image is

compressed by using lossy and lossless methods for

different types of images. Here, the lossy compression

is done by the fractal decomposition code and lossless

compression is done by using the LZW algorithm.

LZW is the dictionary based algorithm, which is simple

and can be used for the hardware applications. Fractal compression represents the image in a contractive form.

Inspite of its lossy nature it can be used for the case of

lossless compression. A general comparison is done

based on analyzing the parameters such as Peak Signal

to Noise Ratio (PSNR), Mean Square Error(MSE),

Image fidelity (IF), Absolute Difference (AD) to the

different types of images.

IndexTerms Image compression, LZW, Fractal decomposition, mean square error.

1. INTRODUCTION

In the digitized world of today, the role played

by computer and its applications are mandatory in each

and every field. There are many fields which has the

wide variety applications of the audio, image and

digital video processing. In order to handle more

number of data (images, videos) there is a requirement

of large amount of space and a huge bandwidth for the

process of transmission. The good solution for this problem is the compression of the images which reduce

the redundant information and increase the space.

In this paper, LZW algorithm is capable of

producing compressed images without having an effect

on the quality of the image. This can be successfully

brought about by reducing the total number of bits

needed to constitute each pixel of an image. Thus, in

succession which minimize the memory space needed to store images and transmission can be done with little

amount of time. There are two types of image

compression. They are lossy and lossless image

compression. Depending on the application and the

degree of compression any one of the two types can be

chosen. Lossless compression is used where the exact

replica of the original image is to be produced. Lossy

compression can be affected by the loss of data

compared to the original image. The improvement of

this type is that it provides a scope for high

compression ratios than the lossless compression

Fig1.Block diagram of image compression system

The most common characteristics of the

images are the nearby pixels are compared and then

they have the unwanted information. The first quest is

to find reduced number of similar depiction of the

image. The two major elements of compression are

redundancy and reduction in irrelevancy.

Reduction in redundancies aims in getting rid

of the mimeo from the source signal. Reduction in the

irrelevancy neglects the part of the signal that is not

seen by the receiver or the Human Visual Display

System.

Original

image

Encoder

Channel

Recreated

image

Decoder




2. BLOCK DIAGRAM

Fig.2Block diagram for the proposed system

The block diagram consists of the input image.

At first the input image is to be compressed by the

LZW algorithm. In order to be compressed by LZW it

must be transformed to binary image. The grey scale

image is to be converted from the decimal value to

binary value. The binary image which is compressed by

LZW is then divided into blocks which have 7 bits

each; since it wants only 7 bits to depict a byte. This is

known by the term decoding by BCH. Thus the compressed image is obtained.

Then the reverse process of decoding is to be

done to delete the extra added 7 bits. Then the result so

obtained is to be decompressed to get the binary image.

To obtain the original image, the binary image is to be

transformed to grey scale image.

3. PROPOSED METHOD

The proposed method uses a compression

methodology using the two lossless techniques LZW

along with Huffman coding and then the Discrete

Cosine Transform (DCT). Next, along with these

lossless techniques the proposed method also has the lossy algorithm as fractal compression. Fractal

compression algorithm removes some information from

the input image and the output given by the fractal

method is not so clear. DCT algorithm produces a

blurred output. LZW algorithm produces the result

which is same as that of the original image. The LZW

algorithm is superior to other compression techniques.

3.1 LZW ALGORITHM

The LZW algorithm is named after the

scientists Lempel, Ziv and Welch. It is a simple

dictionary based algorithm used for the lossless

compression of images. Dictionary based algorithms

are nothing but they are arranged in the form of dictionary. The algorithm first searches the file and then

it arranges the dates in sequences of strings which occur

repeatedly. The LZW algorithm then replaces the

repeated text omitting the incoming text. If any one of

the data is found to be new then it will add to the

dictionary. These words are then saved in the dictionary

and the references are added where the data gets

repeated. Each word in the dictionary has a particular

code. The repeated words are replaced with another

code. The length of the code must be a constant one.

The LZW algorithm is used where the file have more repeated strings. It s a computationally fast algorithm

and is very effective, since the decompression does not

need the strings to be passed to the table. LZW

encoding is based on the multiplication of the encoded

pixels. The principle involves in building the dictionary

by substituting the patterns for the image given as input.

The LZW algorithm can be applied to different types of

image formats which are used to remove the repeated

strings. The BCH algorithm used along with the LZW

algorithm is to correct the errors or to find the errors.

The size of the image file which is compressed by LZW algorithm along with BCH increased because it has

monochrome images.

Input Image Compress by

LZW

Decode by

BCH

Compressed

image

Original

image

Decompress by

LZW

Encode by

BCH




3.2 DISCRETE COSINE TRANSFORM

DFT has a good computational efficiency but the designing of DFT is difficult and has poor energy

compaction. Energy compaction is nothing but the

capacity to collect the energy of the spatial coordinates

in the frequency domain. Energy compaction is very

much important for image compression. Since the DCT

does not save any bits and also doesnot introduce any

distortion hence it can be quantized and used in lossless

compression.

The DCT works well in separating the image

into different pixels of differing frequencies. So that it

can be compressed without losing the major

information. The edges and borders in the images

compressed by DCT are clearly visible without any

blurs and distortion. In the processing of the image by

DCT, the image is first broken into 8*8 blocks of

pixels. Then from the top to bottom or left to right DCT

is applied to each and every block of pixels. The blocks

of pixels are compressed by the process of quantization.

The compressed block of array which has the image is stored in less space than the original image. To obtain

the original image is done by the process of

decompression which can be done by Inverse Discrete

Cosine Transform (IDCT). DCT and ICDT are

symmetric in nature.

Before applying DCT to the image the pixels

are to be divided based in the black and white pixels.

The black and white pixels range from 0 to 255. The pure black pixels are denoted by 0 and pure white

pixels are given by 255. This is the reason why the

image looks like black and white or grey in color. An

image contains thousands of 8*8 blocks in which the

compression is done in each and every block. By this

way each and every block is to be compressed and the

resultant image is obtained.

4 FRACTAL DECOMPOSITION ALGORITHM

The Fractal image compression is given by

Integrated Function System (IFS). Here in this method

it has a source image and the designation image. The

source image is known as the attractor. The designation

image is the output or the recreated image. At first the

image is partitioned into small parts which are known

as blocks. Those subdivided blocks should not overlap with other blocks. Each destination block is to be

mapped with other block which is assembled after the

removal of repeated bits. It has a transforming operator

is known as contracting function. It transforms the

compressed image but the visual effect does not

change. This point is reached when the transformation

is done to N points in the image which can be done by

elementary transformations. This has the basic

approaches needed to compress the image known as

contacting transformation. Then by dividing and

contacting the image by a transformation it is named as

fractal transformation or fractal decomposition. It is

advantageous since it depicts the image in a contractive

form. Fractal compression is a recent method on lossy compression based on the use of fractals which

degrades the likeliness of different parts of an image.

5 PERFORMANCE CRITERIA FOR IMAGE

COMPRESSION

SNR:

The standardized quantity of measuring the

image quality is the signal-to-noise ratio. It is given by

ratio of the power of the signal to the power of noise in

the signal. SNR is given in decibels by

𝑆𝑁𝑅 𝑑𝑏 = 10 log10

σx2

MSE

PSNR:

The most common case of representing the

picture of the input image is given by the Peak value of

SNR. It is defined as the ratio of the maximum power

of the signal to the power of the corrupted noise signal.

𝑃𝑆𝑁𝑅 𝑑𝑏 = 10 log10

2552

𝑀𝑆𝐸

Where the value 255 is the peak in image signal.

MSE:

Mean square error is defined as the measure of

average of square of ratio of estimator output to the

estimated output. it is also known as the rate of

distortion in the retrieved image. Mean square error is given in decibels by

𝑀𝑆𝐸 𝑑𝑏 =1

𝑥𝑦 𝑋 𝑚, 𝑛 − 𝑌(𝑚, 𝑛)2

𝑦−1

𝑛=0

𝑥−1

𝑚=0

6 RESULTS AND DISCUSSION

The performance comparison between lossy

and lossless images is done using MATLAB. The lossy compression is done by using fractal decomposition

method and lossless compression is done by two

compression algorithms DCT and LZW.

In this paper the input image is of different image formats are taken and loaded into the system

for the compression of the given input image. At first




the corresponding loaded input image is displayed. This loaded image is further preceded to the next

stage of lossy compression. The next stage gives the

compressed image by fractal decomposition method.

The loaded input image is converted to grayscale

values and then the binary values are obtained from it, then the binary values are converted to compressed

image. Next upcoming step gives; the image which is

compressed by the fractal decomposition method is

then compressed by the lossless compression

technique of DCT algorithm. This provides a better

result than the fractal compression method. The

image which is compressed by the DCT algorithm is

then compressed by LZW algorithm which is a

lossless method. This provides a better result than the

DCT algorithm. Further, image which is compressed

by the DCT algorithm is then compressed by LZW

algorithm which is a lossless method. This provides a better result than the DCT algorithm.

This algorithm based on the combinational method has the combination of fractal decomposition

for lossy method and DCT, LZW for the lossless

compression. Here in this thesis different image types

such as bmp, tif, png, jpg formats are used .those

image formats are black and white type. The given

colored images are processed in the form of gray

scale images only.

Input Image Image obtained by fractal method

Image by DCT Image by LZW

Fig3 Result of Compressed Image of bmp Type



Fig4 Result of Compressed Image of tif Type



Fig5 Result of Compressed Image of png Type






Fig6 Result of Compressed Image of jpeg Type

PARAMETERS VALUES OBTAINED

LOSSY COMPRESSION BY FRACTAL

DECOMPOSTION

Average absolute

difference

0.2198

Image fidelity 0.1851

SNR 7.3152

PSNR 9.8407

MSE -0.0717

LOSSLESS COMPRESSION BY LZW

Average absolute

difference

0.0105

Image fidelity 0.0004

SNR 3.1696

PSNR 5.7365

MSE -0.0001

Table1: Summarized Result

IMAGE

TYPE

IMAGE

NAME

PSNR SNR MSE

bmp bird 5.73 3.16 0.0001

tif women -20.23 -25.39 0.10

png balloon -22.64 -22.91 0.18

jpeg penguin 9.87 7.35 0.07

Table 2 Comparison of different image types

7 CONCLUSION

Thus the compression is a theme which gains

much significance and it can be used in many

applications. This thesis presents the lossy and lossless

image compression on different file format of images. Many different types of methods have been assessed in

account of quantity of compression that they offer,

effectiveness of the method used and the sensitivity of

error. The effectiveness of the method used and the

sensitivity to error are sovereign of the feature of the

group of source. The level of the compression attained

greatly depends on the source file. It is terminated that

the higher data redundancy favors to reach more

compressed image. The proposed method has the

advantage of LZW algorithm which is combined with

the fractal decomposition method is known for the clarity and fastness. The major goal is to reduce the

computational time and minimize the space occupancy.

The tests were carried on the different types of

image sets and their results were assessed by the clarity

and then by bits per pixel. The demonstrational rating

gives that the proposed method has improvement while

comparing with other conventional methods.

8 FUTURE WORKS

The future works aims in achieving a better

compression ratio by using various new techniques. The

proposed method is on various image types but it is

limited to the videos. New algorithms can be merged and resolved that reduced the computational time which

occurred by the creation of dictionary in LZW method.

The dataset used in this thesis is restricted;

thus by applying the new algorithms on a larger dataset

could be the theme for the future research. The

algorithm can be elaborated for the compression of

color images. Also the work can be enlarged to video compression. The data in video is three dimensional

collections of the colored pixels that have the temporal

and spatial redundancy.




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Sree Thayanandeswari received the

B.E degree in Electronics and communication from

Anna University ,Chennai, 2007 and M.E degree

from Anna University ,Chennai, 2013. She is

currently working as an Assistant Professor in the

PET Engg college,Department of Electronics and

Communication, Vallioor. Her research areas

include digital image processing.

Jeya Christy Bindhu Sheeba received the B.E degree in Electronics and communication

from Anna University ,Chennai, 2014.She is

currently doing her M.E in in the PET Engg college.

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