image compression method 2014

8/11/2019 Image compression method 2014

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Image Compression through Data Representation in

Frequency Domain.

Elda CINA1,1Information echnology

Faculty,!Ale"sander #oisiu$,%ni&ersity of Durres,

Durr's,Al(aniaemail)elda.cina*uamd.edu.al

Esmerald A+IA1-

1Information echnologyFaculty,!Ale"sander #oisiu$,

%ni&ersity of Durres,Durr's,Al(ania

esmeraldaliai*yahoo.com

a(i( A#A#-,/

Faculty of Engineering, %ni&ersityof #oncton, Canada

/0chool of Engineering, Canadian

Institute of echnology, Al(aniaa(i(.amam*umoncton.ca

Abstract Data compression is a very important field these days.

You can find it in any web page, network devices, application filesetc. Its importance has grown during the years as a necessity of

storage and time of transportation. Images are one of the most

important data types to take in consideration. Because of the

large diversity of images in terms of both size and content, none

of the existing compression methods can be presented as

appropriate for all of them. s such it is always an open field for

discussions. In this paper we introduce a new way of compressing

images. !e propose a lossy techni"ue based on the method of

#Data $epresentation through combinations%, applied in the

fre"uency domain. !e use the Discrete cosine transform &D'()

and the indexing properties as key points for our techni"ue.

ccording to this techni"ue we enable not only compression but

also other security measures like cryptography, steganography

and watermarking.

Key words* Data compression, fre"uency, index, D'(,

information security.

I.INR2D%CI2N

Image compression is a &ery discussed topic in

computer science and information processing. Ne3

technologies of cameras use (igger and (igger

resolution. As a consequence, compression is ano(&ious need starting from human personal use to

3e( de&elopment.

4e can compress images o&er one of t3o types of

compression.

+ossless compression 3hich is important forapplications requiring precision such as

medical scanning, astronomy.

+ossy compression 3hich is 3idely used ine&eryday life, for instance in 3e(

de&elopment.

he first type of compression is performed

according to a lo3 compression rate of around 567or lo3er 819 and 3e o(tain the original image after

decompression as is 3ithout any alteration.

he second method gi&es high compression rate,

(y reducing redundancies, remo&ing duplication or

irrele&ancies, and omitting information 3hich 3ill

not (e noticed from the human &isual system

:;0 technique, (efore

performing compression, the image is di&ided in

(loc"s, !le&el shifted$ (y 2P1and then

transformed from space domain to frequency using
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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the DC8-98/9, 3hich is the most popular frequency

transformation. %ntil no3 nothing from the

information is lost. he net step is uniform mid

tread quantiation using a fi 3ell studied sample.

he quantied coefficients are calculated as follo3s

6.5ij

ij

ij

lQ

+

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According to the data representation through

com(inations theory, e&ery signal can (e

represented (y an array of samples or its

corresponding inde from the com(ination ta(le859.

his ta(le is (uilt from all possi(le com(inations ofimages of sie #N piels. Each piel has a &alue

from 6 to +1 :color le&el, for eample gray le&el


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he flo3 diagram of Figure 1 gi&es a description on

the steps 3e are applying to implement

compression. It goes through these steps)

1. Di&ide in (loc"s and for each (loc"

a. #a"e DC transform(. ?uantie

c. In&erse Migag

d. Remo&e eros from the left side of

the o(tained num(er

e. Find the inde

-. Asem(le the indees and send the file

Hy the other hand at the recei&er or the

decompression process)

1. Etract the com(ination from the indees

and for each com(ination

a. Add trailing eros(. Migag

c. #a"e in&erse DC transform

-. Re(uilt the full image

Figure 1. Encoding Decoding =rocess

;. RE0%+0

A num(er of eperiments ha&e (een carried out in

order to e&aluate the performance of the proposed

Algorithm. Different sies and contents ha&e (een

tested. 4e ma"e a comparison 3ith a &ersion of

=E> standard 3ith Run+ength Encoding :R+E< to

gi&e a complete panorama of the ad&antages our

method offers.

-riginal Image $econstruction D'( with $/

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In a(le - are sho3n the results of the a(o&e image

tests, 3here 'rstands for Compression rate, $34

for Root #ean Error,

145$for =ea" 0ignal to Noise Ratio, and 44I3

for 0tructural 0imilarity Inde.

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a(le -. Compression ratios of our technique compared to those offered (y DC using R+E

As clearly pointed out in a(le -, our techniquegi&es higher compression rates, generally at least B

times higher than the DC 3ith R+E. his is &alid

for the first le&el of compression. According to the

need of users 3e can go further on o(taining higher

compression rates (ut 3ith larger error.

Furthermore, 3e ha&e used another approach to

o(tain higher compression rates. After applying the

DC and Migag path 3e o(tain eros at the

(eginning of the &ector. 4e should remo&e them. In

addition to remo&ing only trailing eros 3e can

remo&e more relati&ely lo3 &alues. In this case 3eo(tain a higher compression rate (ut 3ith some loss

of data. Hy applying the trial and error method, 3econcluded that the optimal situation consists in

lea&ing only 1L DC coefficients for indeing,

3hich results in a compression rate of 1-.)1. his

is &alid for image no matter ho3 large it is. he

highest compression rate could (e 1L)1, if 3e

analye only DC coefficients (ut errors are

noticea(le (y na"ed eye. o3e&er, it is up to userOs

need in terms of image quality or compression rate

to choose 3hich is the most appropriate for them.


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image compression method 2014

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