Hybrid Chaotic Magic Transformation with …A s calable coding technique is suggested by Zhang et al. [12] for compression of encrypted image via a multi -resolution construction

Hybrid Chaotic Magic Transformation with

Advanced LZW based Encryption-then-

Compression of Images 1N.Mahendiran, 2Dr.C.Deepa

1Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts & Science, (Formerly

SNR Sons College), Coimbatore, Tamilnadu, India

2Associate Professor, Department of Information Technology, Sri Ramakrishna College of Arts & Science,

(Formerly SNR Sons College), Coimbatore, Tamilnadu, India

[email protected],[email protected]

Abstract: In the field of information security, image encryption

plays a vital role. In order to communicate the confidential

information, encryption of images is proved as a successful

method, for which countless procedures are discovered. Images

have to be encrypted prior to compression, in few practical cases.

A hybrid Chaotic Magic Transformation (CMT) with Advanced

LZW (ALZW) based encryption-then-compression scheme is

brought-in in our work for hiding the information to safeguard

the information. With the help of the median filter, the input

images were preprocessed initially, in order to eliminate the

unwanted noise to give a quality image. Then, to encrypt the

image into various blocks, the de-noised image is segregated

into several segments. Through hybrid CMT, these blocks of

images were encrypted. For compressing the encrypted image,

we make use of the ALZW based lossless compression scheme,

to minimize the space of the image. From the experimental

analysis, it is confirmed that the suggested hybrid CMT with

ALZW acquires better results when distinguished with the

current Improved LZW (ILZW) and LZW compression schemes

in terms of Compression Ratio (CR) and Peak-Signal-Noise-

Ratio (PSNR).

Keywords: Encryption, compression, median filter,

segmentation, chaotic magic transformation, huffman coding,

I.INTRODUCTION

A great deal of concern in the fields of secure transmission

and compression of images is increased because of the quick

demand if image transmission through public network like

social networking sites. It is required to minimize the size of the

images before being broadcasted, because of few inherent

features in digital images like high correlation among

neighboring pixels and having bulk data capacity. In the

interim, the secured way of image transmission has emerged

the requirement of combining image encryption and

compression. Let’s assume a practical scenario, where content

owner (say, Alice) requires to efficiently broadcasting the

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 3133-3147ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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image Ito recipient (say, Bob) via an insecure bandwidth-

constrained communication channel provider (say, Charlie).

Compressing the data and then en-crypt with the help of

any secret key, is the conventional way for data reduction and

protection, where, Alice compresses original image I into Ic

initially and then encrypts into with the help of secret

key. Encrypted data will be forwarded to Charlie, who

simply passes it to Bob. Being an authorized user, Bob does

the successive decryption and decompression get the re-

constructed image .

For compression–encryption techniques viz. techniques

based on Compressive Sensing (CS), various have been stated

in literature, where the property of sampling and compression

is possessed at the same time. Later, the focus has turn on

whether CS can be brought-in in image encryption algorithms.

Y.Zang et al[1], gave an assessment on CS based techniques

for information security, where it examines the three main

factors of security such as image encryption based on chaos

and CS , encryption based on CS and optics and the

encryption techniques based on CS, optics and chaos. Huang

et al. [2] explained a parallel image encryption technique based

on CS, where the cipher structures comprises of scrambling,

mixing, S-box and chaotic lattice XOR is established to further

en-crypt the quantized data. A hybrid compression and

encryption technique with key-controlled measurement matrix

in CS is established by Zhou et al. [3]. Additional, a new

hybrid compression encryption algorithm based on CS is

explained in [4] in which measurement matrix is built as partial

Hadamard matrix and managed by a chaos index sequence. An

image compression and encryption technique based on 2D CS

and fractional Mellin transform (FrMT) is suggested by Zhou

et al.[5], where it makes use of the nonlinearity of FrMT to

oppose the common attacks and control the compression

capability of CS.

An image compression encryption scheme based on hyper-

chaotic system and 2D CS is proposed by Zhou et al. [6],

which achieves the compression and encryption at the same

time. Through the cycle shift operation, which is controlled by

the hyper-chaotic system, the CS will minimize the data amount

and then the compressed encrypted image is re-encrypted. A

new technique is suggested by Alfalou et al. [7], where the

compression and encryption were executed at the same time in

a dependent way, which is nothing but a combination of

spectral fusion based on the properties of Discrete Cosine

Transformation (DCT). Zhu et al.[8] provided an effective

technique which utilizes the hyper-chaos and Chinese

remainder theorem, where the 2-D hyper chaos helps to mix up

the original image and Chinese remainder theorem and it is

enforced to spread and compress the shuffled image at the

same time. In [9], an optical algorithm for simultaneous

compression and encryption of phase-shifting digital

holograms for 3-D object reconstruction is explained, which

works according to the spectral fusion.

The above addressed compression–encryption a scheme

satisfies various secure transmission cases. But, in new

applications, the conventional way has to be revisited and its

knowledge is acquired with an example. For instance: assume

that Alice requires transmitting the information to Bob,

whereas Charlie is the network provider. Alice requires

safeguarding the privacy of her data from everyone even to

the Charlie through encryption. She has restricted

computational resources, in order to minimize the size of data.

So, before encryption, she won’t make use of her limited

computational resources to minimize the size of image and it is

strangely true when Alice utilizes a resource-divested mobile

device. Char-lie has an overriding interest to increase the

usage of network by compress ing all network traffic

counterclockwise. Alice hides the secret key which is utilized

to encrypt the image data from Charlie, since she doesn’t trust

on Charlie; on fully encrypted data, Charlie does the

compression and finally it will be pass to BoB. Joint

decompression and decryption is done by Bob, in order to re-

build the image from the encrypted and compressed data.

Compressing the images in fully encrypted domain is the great

International Journal of Pure and Applied Mathematics Special Issue

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dispute within such Encryption-then-Compression (ETC)

system.

For compressing general purpose digital images, lossy

compression methods were utilized, where minor loss of data

isn’t an obstacle but, lossless compression presenting an

image in the smallest number of bits without losing any

information. For image compression, we concentrate on

lossless compression scheme. Zhang designed an image

encryption scheme through pixel-domain permutation and

explained that the encrypted file can be effectively compressed

by eliminating the excessively rough and fine information of

coefficients in the transform domain [10]. A new compression

approach for encrypted images, were proposed by Zhang et.

Al[11], which works according to the multi-layer

decomposition. For effectual ETC method [12-18], different

techniques have been developed.

The current ETC systems still fall considerably short in the

compression performance when distinguished with the state-

of-the-art lossless image coders that demand unencrypted

inputs, in spite of extensive efforts in recent years. Designing a

pair of image encryption and compression schemes for images

is the main attention of our work. This scheme concentrates

much on the hybrid CMT encryption with advance LZW

compression for images. From experimental analysis it is

confirmed that the proposed system acquires good result

when distinguished with the current LZW and Improved LZW

(ILZW) compression schemes. The other session of this paper

is organized as follows: Section 2 explains the current ETC

scheme for images. Section 3 provides the details of our

proposed ETC system, where lossless compression is

considered. In Section 4, Experimental analysis was stated to

validate our findings. Section 5 presents the conclusion.

II. RELATED WO RK

Different ETC exiting schemes and their limitations has been

explained here. A scalable coding technique is suggested by

Zhang et al. [12] for compression of encrypted image via a

multi-resolution construction. Original pixel values were

masked with the help of the addition modulo-256 with pseudo

random numbers in X.Zang[12]. Through number of iterations,

this work is scalable and performs well. The down sampling

based techniques were stated in literature [13-15], apart from

the CS and quantization based techniques, in order to minimize

the size of encrypted images up to a preferred level viz. a new

compression technique for encrypted image is again

suggested by Zhang et al. [13] via the multilayer

decomposition.

A novel compression technique for encrypted images is

suggested by Zhang et al. [14], for generating little auxiliary

information with optimized quantizing parameters to enhance

the compression performance. The prediction error clustering

and random permutation helps to achieve encryption and it is

explained by Zhou et al. [15], where the compression

performance is examined for both lossless and lossy

compression proposes an efficient ETC technique. A new

encryption then compression technique using rate distortion

optimization is given by Wang et al. [16], which distinguishes

it from the others.

Vaish et al. [17] suggested a prediction error based ETC

technique, were the prediction errors were computed with the

help of a sub-image and efficient compression is accomplished

through quantization and Huffman coding. Kumar and Vaish

[18] explained that an efficient compression of encrypted image

is acquired with the help of wavelet difference coding.

A Tangent-Delay Ellipse Reflecting Cavity-Map System (TD-

ERC), wavelet neural networks (WNN), and XOR operation on

binary data is suggested by Zhang and Fang [19], which

accomplished cipher image. Here, addressed attacks are as

follows: key size being 10195, histogram analysis, correlation

analysis, and differential analysis. The proposed system can

be enforced to give the secured information

Symmetric chaotic economic map (CEM) is brought-in by

Askar et al., [20], with key space 1084, the entropy that closes

to ideal value 8, and low coefficient correlation that closes to 0.

A chaotic sequence is created with fraction decimal values to

integers by CEM. The following were the attacks which are


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mentioned earlier, they are: key sensitivity analysis, correlation

analysis, and analysis of information entropy.

The chaotic cat map algorithm is chosen by Kanso and

Ghebleh [21] and it is utilized for medical image security

applications with rounds and every round has two phases:

shuffling and masking enforced for block level and also the full

image. The pseudo random matrix of the same size is utilized as

an input image to maximize processing speed, for masking

phase of every round. For medical image robustness, statistical

cryptanalytic attacks like key search and differential attacks

were examined and for ROI and for full image, same encryption

and decryption technique were enforced and also it

accomplishes the same level of security in ROI and full image.

Examining the brute-force attack by assuming the key space is

huge. But, the author didn’t mention the decrypted image

quality and information entropy.

The new 2D-Sine Logistic Modulation Maps (2D-SLMM)

based on logistic and sine maps with effective image pixel

shuffling algorithm called as Chaotic Magic Transform (CMT)

is suggested by Hua et al., [22], in order to derive random pixel

property encryption image. Generally, high redundancy data

will be there in the digital images, because of the high

correlation of pixels. CMT helps to break these correlations

and this will modify the pixels values in random position. 2D

chaotic maps have good performance with respect to

generating chaotic sequence than 1D chaotic map, but they

require comparatively difficult hardware structure and cost. At

the time of shuffling when compared with early chaotic maps,

CMT’s performance is good. With the help of following

parameters, chaotic performance is examined: trajectory,

Lyapunov exponent, and Lyapunov dimension and

Kolmogorov entropy surviving chaotic maps were generally

classified into 1D chaotic maps and high-dimensional maps.

Chaotic Map Lattices (CML) had weakness in conversation of

floating values into pixel value which leads to data loss in

image and it is explained by Jasteazebski and Kotulski [23].

Jasteazebski and Kotulski suggested Improved CML, which

works according to the CBC method but lacks from different

security services like noise attacks, differential attacks, and

statistical attacks. Image encryption hides few particular

issues, for instance, huge size of image pixels and redundancy.

The value of pixel in encryption process will depend on the

neighbored pixel value, that is, pixels blocks, in few scenarios.

The brute-force attack happens, because of the small key size.

The time complexity, space complexity, noise attacks,

differential attacks, statistical attacks, and so forth were

conceived here. Encryption is advanced based on modular

arithmetic operator, on the medical images and it is explained in

J. B Lima [24]. The Current ETC techniques still fail in

compression performance when [32] distinguished to the state-

of-art lossless or lossy image compression techniques, despite

of many attempts in the past few years.

III. PRO PO SED METHO DO LO GY

The proposed hybrid CMT encryption with advanced LZW

compression schemes is explained in this section.

A. System Overview

Figure 1 explains the process proposed ETC scheme work flow.

Initially, the Digital Imaging and Communications in

Medicine (DICOM) images were preprocessed to discard the

noise. Then, with the help of the vertical and horizontal

segmentation, the de-noised image is segmented or

decomposed into 4 blocks. Through new block image

encryption scheme based on hybrid chaotic magic

transformation approach, encryption process takes place.

Utilizing the using ALZW method and Improved Huffman

Coding (IHC), the lossless compression is performed. At last,

the recovered image is displayed.


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Fig.1 Work flow of Proposed ETC Scheme

B. Preprocessing

Here, DICOM brain images are considered as input. To

enhance the unwanted distortion in image, pre—processing

technique is utilized here. The noise filtering method is utilized

here to enhance the input image quality. Without minimizing

the sharpness of the image, it has the ability to remove the

noise. In image compression and decompression, median filter

is its edge preserving property, which is considered as the

best features. Median filter assumes every pixel in the image

and search at its nearby neighbors to decide whether or not it

is representative of its surroundings. This filter will replace the

pixels with the median rather than replacing the pixel value with

the mean of neighboring pixel values. By sorting entire pixel

values from the surrounding neighborhood into numerical

order and then replacing the pixel being conceived with the

middle pixel value, the median value is computed. (If the

neighborhood under consideration has - an even number of

pixels, the average of the two middle pixel values is used.) A

3×3 square neighborhood is utilized for creating more severe

smoothing.

C. Segmentation

With the help of vertical and horizontal segmentation, the

de-noised image is segmented or decomposed into 4 blocks.

Consider that the size of the input image is N × N. Classify the

input image into 4 blocks, block 1, block 2, and block 3 and

block 4 where every block is the size of N/4 since the size of

the image is 256 × 256 with the help of vertical and horizontal

segmentation. Every block has 64 vertical and horizontal lines.

D. Hybrid CMT Based Encryption

Through new block image encryption scheme based on

hybrid chaotic magic transformation approach, encryption is

done. Chaotic research for an image encryption has an

importance, because of the sensitive dependencies on initial

conditions, system parameters, random behavior, non-periodic

and topological transitivity, and so forth; the chaotic systems

assists for encrypting the image, which can’t be identified by

the malicious users. The image will not be recognized, even if

the attacker is intercepted, so it can transfer successfully over

the Internet which assures the security of image

communication. Various encryption methods haven’t

mentioned the security services like pixel correlation, chosen-

plaintext attack, cipher attack, histogram analysis, and entropy

[25-27]. So, a hybrid Chaotic Magic Transformation (CMT)

approach is suggested to give more robustness for

safeguarding the images from different attacks such as key

space analysis, key sensitivity, pixel correlation, histogram

analysis, chosen-plaintext attack, cipher attack entropy, and

noise analysis. Lanczos algorithm is utilized in CMT, in order

to create the root characteristics and eigenvectors, so we name

it as hybrid CMT.

Consider plain image P, which is provided as input to the

hybrid chaotic magic transformation encryption process. It has

four steps: image column pixel values were sorted in ascending

order and it does a row sorting. Pixel confusion phase achieves

Input image

Pre-processing using median

filter

Segmentation

Encryption using hybrid CMT

Lossless compression using

ALZW with IHC

Decompressed image


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confusion property by randomly shuffling entire pixel

positions, obtaining confused image matrix M. The overall

process of hybrid CMT is illustrated in figure 2.

In order to generate key (K) with a size of host image, the

Chaotic sequence generator was utilized. This key ( ) is

provided to the Lanczos algorithm, in order to recognize the

vector characteristics, which enhances the key space and

enhance security against the potential attacks Cipher image

matrix (Z) is acquired by performing the multiplication

operation among the key vectors (K) and confusion matrix (M).

It enhances the key space and improves the security against

the potential attack.

Fig.2 Workflow of Hybrid CMT Approach for Encryption

1) Hybrid Chaotic Magic Transform for Encryption

The target of encryption algorithm is to confound the position

of pixels for every block of the image according to the

following steps: Hybrid CMT algorithm shuffles matrix [28]:

Algorithm 1: Hybrid CMT Algorithm

Step 1: Sort each column of in ascending order to obtain

sorted matrix .

Step 2: Generate shuffled index matrix by connecting the

pixels in with locations

with respect to CO.

Step 3: The pixel shuffling process is done by shuffling the

pixels positions to the right in the clockwise directions.

Computation speed of encryption process has been increased

by directly shuffling row by row and column by column

instead of pixel by pixel.

Step 4: The resultant shuffled matrix is . The shuffling

process is done by using the hybrid CMT algorithm; here,

random chaotic matrix with size × is used to produce the

shuffled index matrix of size × , where index matrix is

defined by

Let be the original image with size × and be the

resultant shuffled image. The pixel shuffling process of the

original image is defined by

Where is defined the generation of shuffled indexed matrix

from chaotic matrix and sorted matrix is generated by

sorting each column of chaotic matrix in ascending order.

The index matrix shows the position of data where they are

permuted from chaotic matrix . Where is the original image

matrix and is the resultant shuffled matrix obtained from

HCMT.

Step 5: A linear congruential generator (LCG) is used to

generate × pseudorandom numbers by using

Where and are integers and is the start value.

Step 6: Lanczos Algorithm [29]. The application of Lanczos

algorithm is to perform normalization on large eigenvalues and

eigenvectors. It was invented by Cornelius Lanczos. We used

1 as the random vector, matrix ― .‖ is the characteristic

roots and is the characteristic vectors, for loops being

used to calculate eigenvalues and eigenvectors.

Step 7: Finally, the cipher image has been computed for image

compression. Here, the lossless compression has been

focused and it has been explained in next segment.

E. Image Compression

The lossless Image compression is executed on encrypted

image. With the help of Advanced LZW method, this

Input image P

Column sorting

Row sorting

Pixel confusion

Pixel shuffling

Confusion matrix M

Chaotic key generation

Key matrix K

Lanczos algorithm

Vector characteristic

calculation

Z=M*K

Cipher image Z


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compression process is executed, through Improved Huffman

Coding (IHC). Hybrid compression provides better

compression ratio when compared with the single

compression. We will get a better compression ratio, if the data

image is compressed by Huffman Coding and then by LZW.

So we call it as ―Data compression using Huffman based LZW

Encoding.

A compression technique with the help of the two lossless

methodologies IHC and LAW coding to compress image is

suggested here. The image is compressed with Huffman

coding resulting the Huffman tree and Huffman Code words is

done in the initial stage. Entire Huffman code words were

concatenated together and then compressed with the help of

Lempel Ziv Welch coding, in the next stage. The Retinex

algorithm is utilized on the compressed image for improving

the contrast of image and enhances the quality of image, in the

final stage.

To maintain the image into bit stream as compact as likely

and to display the decoded image in the monitor as exact as

possible, we make use of the Image compression coding

technique. Let’s assume the encoder and a decoder; the image

file will be converted into a series of binary data, which is

names as bit stream, when the encoder receives the original

image file. The encoded bit stream and decodes it to create the

decoded image, when the decoder receives the encoded file.

Image compression takes place if the total number of data

quantity is lesser when compared with the total data quantity

of the original image. In the encoding and decoding process,

image compression technique algorithms were utilized for

compression and the reverse process takes place for

decompression.

1) Huffman Coding and Decoding Process

The probability distribution of the alphabet of the source to

establish the code words for symbols is utilized by Huffman

Encoding Algorithms. In order to compute the probability

distribution, the frequency distribution of entire characters of

the source is computed. The code words were assigned, based

on the probabilities, like, shorter code words for higher

probabilities and longer code words for smaller probabilities. A

binary tree is built, where the leaves are nothing but the

symbols, this is based on their probabilities, and paths were

nothing but the code words. Static Huffman Algorithms and

Adaptive Huffman Algorithms were the two families of

Huffman Encoding and it is suggested here. For both the

compression and decompression processes, static Huffman

Algorithms initially computes the frequencies and then it

creates a common tree. The compression program stores the

count every symbol which appears so far in the source text.

The symbol counts were utilized as estimates of the relative

probabilities of the symbols and a table of Huffman codes

based on these frequencies is built, in order to encode the next

symbol and for encoding, the Huffman code in the table were

utilize. From the de-compressed image pixels, the decoding

algorithm can re-create the same set of symbol frequencies and

it can utilize the table to re-build the same table of Huffman

codes. Hence, it can uniquely decode one symbol, update the

frequency count of that symbol, update its table of Huffman

codes and then decode the next symbol, and so on.

Where Avg is count the average of the probability of all

symbols. P is utilized for probability the input symbols L1 is

the leaf which count the lower probability of the symbols.L2 is

the second lower probability symbols. L1+L2 generate the

parent of the node. This process is iterated while every symbol

was processed. The tree is generated, after processing every

symbol. In Huffman tree entire leaf node allocate the lower

probability and addition of last two leaf nodes and it produce

the parent of the leaf this process can be iterated until the

entire tree is processed. For encoding, Huffman algorithm is

utilized and for decoding the reverse process helps.

The conventional HC can’t assure that entire nodes with

greater weight were added in the higher level of nodes. If it

doesn’t have four uncoded nodes remaining in the end, the

number of child nodes in the root will be deficiency, which


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means, there is no use of the nodes with minimum weight but

the application of the nodes with maximum weight. Then the

average code length will be raised and coding efficiency gets

reduced, resulting in waste.

Improved Huffman Coding (IHC) algorithm is brought-in to

minimize the above mentioned issue and it provides priority to

the nodes with greater weight. These nodes will be shifted up,

closest to the root, so as to make sure both the tree root and

high-level node possess of 4 child nodes, with vacancy only in

the lowest level. The algorithm process is as follows:

Algorithm 2: Improved Huffman Coding (IHC) Algorithm

Step 1: Compute the total number of nodes based on J

characters.

Step 2: Compute the number of child nodes which cannot form

a collection of four by set as k.

Step 3: If k = 0, execute Step (5), otherwise execute Step (4).

Step 4: First, k child nodes with the minimum weight are used

to generate their father node by set as K. Then k nodes are

deleted from the node collection and the father node K is

added to constitute node collection by set as J∗.

Step 5: Construct the rest of Huffman tree based on the

traditional quaternary algorithm with J∗.

Compared with traditional one, the improved Huffman

algorithm shows a significant improvement in compression

ratio. To reduce the execution time and of IHC, the LZW

scheme has been presented. It reduced the code words of IHC

algorithm to increase the processing speed, so this LZW with

help of IHC is named as ALZW.

2) LZW Coding and Decoding

Lempel, Ziv and Welch (LZW) say, that compression algorithm

is Simple, lossless and dictionary based compression algorithm

[30]. The compression and extraction dictionary were both

fixed-length, and its length is 256. Looking for the codes in the

dictionary accept sequential traversal. Nevertheless, the

sequential traversal in the search, every time spending on it is

long so as to increase the compression. A frequently utilized

entries based forward moving concept is suggested to rectify

this issue. The Huffman code words are again compressed

with the help of ALZW is utilized in our work. A real realization

is that including a new variable counter to every node for

maintaining the count of the amount of utilization.

Furthermore, it makes the list bidirectional to navigate the node

easily. When the codes’ amount of usage fulfills the specified

amount, which is a mark of moving, the node is navigated

behind the head node. Hence, non-expandable codes will be

pin-pointed in the front end of the chained lists, which will

speed up the process to look for the codes, thus, save

compression time.

Algorithm 3: Advanced LZW (ALZW)

Step1: Initialize dictionary. Dictionary holds every single

character in the data stream

Step2: Set the particular amount for the spot of moving

Step3: Set the Prefix P Null

Step4: Read the subsequent character in the data stream as the

recent character C.

Step5: Evaluate whether the string P + C is in the current

dictionary.

1) Yes, set P = P + C, that is extending P with C.

2) No.

Output P’s corresponding code to the encoded data stream.

Moreover, the counter of P’s corresponding code adds 1.

Judge the present counter whether meet the mark

a. Yes, move this node behind the last nonexpendable code.

b. No, do nothing

Judge whether the dictionary achieves to the maximum

capacity: If it does not, add the string P + C to the dictionary,

otherwise do not do that.

Define P = C. (P only contains C right now.)

Step 6: Judge whether there are characters in the data stream:

1) Yes, return step3 to continue the encoding process.

2) No, output P’s corresponding code to the encoded data

stream.

End.

To minimize the file size by removing the similarity replication,

the ALZW is utilized. This optimized ALZW accomplishes


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superior compression performance for the providing the input.

Thereby, we have advantages of LZW compression; the size

of files typically raises to a great extent when it includes lots of

repetitive data or monochrome images [31]. The entire work

flow of proposed ALZW algorithm is explained in figure 3. The

proposed scheme step by step process is given below

Algorithm 4: Overall Compression Process

Step 1: Read the image on to the workspace of the mat lab.

Step 2: Call a function which will find the symbols (i.e. pixel

value which is non-repeated).

Step 3: Call a function which will calculate the probability of

each symbol.

Step 4 : Probability of symbols are arranged in decreasing

order and lower probabilities are merged and this step is

continued until only two probabilities are left and codes are

assigned according to rule that the highest probable symbol

will have a shorter length code.

Step 5: Further Huffman encoding is performed i.e. mapping of

the code words to the corresponding symbols will result in a

Huffman codeword’s

Step 6 : Concatenate all the Huffman code words and apply

LZW encoding will results in LZW Dictionary and final

encoded Values (compressed data).

Step 7: LZW decoding process applied on Final Encoded

values and output the Huffman code words

Step 8: Huffman Encode value is applied on the LZW

Encoding process.

Step 9: In final apply the Multiscale Retinex Algorithm on

compressed image to enhance the quality and color of the

image.

Step 10: At last, the Recovered image is generated

Fig.3 Workflow of Proposed LZW with IHC

F. Image Enhancement Using Retinex Algorithm

To improve the image contrast for dynamic compression,

Retinex algorithm is utilized. For explaining the human’s visual

model, and to develop the illumination invariance model,

Retinex theory is brought-in by Land, for which the color has

nothing to do with. The target of Retinex model is proceed the

image reconstruction, and creating the image after

reconstruction the same as the observer saw the images at the

scene. According to the reflection imaging illumination model,

Retinex model works and it is similar to homomorphism

filtering: irradiation light is more smooth when compared with

the modifications of reflected light, you can utilize the low-

pass filter to predict the fuzzy computing on the input image;

reflected light is classified from the input image and smooth

images. The retinex algorithm steps are listed below

Algorithm 5: Retinex Algorithm

Step 1: Do smoothing

Step 2: Increase brightness

Step 3: Incidence component L should be as close as possible

to the output brightness of the image.

Cipher image Z

Compression using Improved

Huffman coding

Reduce Huffman code words

using ALZW

Compressed image

Decoder image

Decode using ALZW coding

Decode using improved

huffman coding

Retinex algorithm

Recovered image


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Step 4: Incident light in the image borders should have

smoothness similar constant.

Step 5: Filter the similar contents

Step 6: Enhance the image quality

IV. RESULTS AND DISCUSSIO N

DICOM brain images computed the suggested image

compression, where 760 images were used for computation.

Digital Imaging and Communications in Medicine (DICOM) is

a standard for handling, storing, printing, and transmitting

information in medical imaging. Every image has the size 256 x

256 and wholly 65536 pixel sizes with a resolution of 96dpi.

Some of the Samples are as shown in figure 4.

For different DICOM images, the performance of the

suggested hybrid CMT with ALZW system is done. The

proposed system performances were distinguished with the

current ILZW [32] and LZW based lossless image

compression algorithms. The proposed image compression

and encryption technique has been executed in the working

platform of MATLAB.

Fig.4 Input DICOM image

Fig.5 Preprocessed image

The DICOM image is considered as an input, which is shown

in figure 4. To eliminate the noise from input image median

filtering is utilized. The preprocessed image is shown in figure

5.

Fig.6 Segmented image

In figure 6 preprocessed images is divided into 4 blocks with

the help of vertical and horizontal segmentation.

Fig.7 Encryption process

The hybrid chaotic magic transformation based image

encryption is executed in figure 7. The image column pixel

values were sorted in ascending order and it does the row

sorting, in encryption process. By randomly shuffling entire

pixel positions, obtaining confused image matrix, Pixel

confusion phase accomplishes the confusion property.

Filtered Image

Input Image

Image div ided in blocks:


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Fig.8 Lossless compression process using ALZW with IHC

With the help of the Advanced LZW method through

Improved Huffman Coding (IHC), the lossless compression is

performed, and it is explained in figure 8. Through advanced

LZW approach, the Huffman codes were minimized.

Fig.9 Decompressed image

The above compression processes were reversed to acquire

a decompressed image, which is shown, in figure 9.

Performance Measure

The performance of suggested hybrid CMT with ALZW

based compression algorithm is distinguished with the current

ILZW and LZW based compression algorithms with respect to

PSNR, compression ratio, execution time and MSE. The

proposed system accomplishes 1.98% of compression ratio,

which is 0.4% and 0.77% higher than the ILZW and LZW

methods.

The MSE of proposed system accomplishes 2.2%, which is

0.94 % and 1.72%lower than the ILZW and LZW methods. The

proposed hybrid CMT with ALZW method gives the higher

PSNR results of 46dB , while other algorithms like ILZW and

LZW gives the results of 1 dB and 8dB correspondingly. The

execution time of proposed hybrid CMT with ALZW system

accomplishes 6.5sec, which is 0.3 sec and 0.7sec higher when

distinguished with the ILZW and LZW methods.

Compression Ratio (CR)

The compression ratio is calculated with the help of below

equation

The above equation determines the ratio among the size of the

original image and the size of the encrypted image.

Peak Signal to Noise Ratio (PSNR)

PSNR is indicated as Peak signal to Noise Ratio. PSNR is

opposite to MSE, that if the small value of PSNR means that

the removal of noise in the image doesn’t give good result.

Mean Square Error (MSE)

MSE is known as the cumulative squared error among the

trampled and the real image. The formula is as follows

MSE= (2)

Where I(x,y) is called as the real image, I'(x,y) is called the

estimated version (i.e. the decompressed image) and M,N are

known as the magnitudes of the images. A lesser value for

MSE signifies less error, and as conceived from the inverse

relation amid the MSE

1 2 3 4 50

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Number of images

CR

hybrid CMT with ALZW

ILZW

LZW

Fig.10 CR Comparison among all ETC Schemes

From the Figure 10, it can be noticed that the comparison of

CR for all ETC schemes. In x-axis the number of images is

considered and for y-axis CR is considered. The proposed

hybrid CMT with ALZW and current methods were calculated.


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The proposed scheme acquires high CR when compared with

the ILZW and LZW schemes. The proposed scheme acquires

better CR, because of the effectual preprocessing and

encryption schemes.

1 2 3 4 50

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

-3

Number of images

MS

E


ILZW

LZW

Fig.11 MSE Comparison among all ETC Schemes


MSE for all ETC schemes. In x-axis the number of images is

considered and for y-axis MSE is considered. The proposed


The proposed scheme reaches less MSE when distinguished

with ILZW and LZW schemes. The proposed scheme reaches

better MSE, because of the effectual preprocessing scheme,

Fig.12. PSNR Comparison among all ETC Schemes


PSNR for all ETC schemes. In x-axis, the number of images is

considered and for y-axis, PSNR is considered. The proposed


The proposed scheme attained high PSNR distinguished with

ILZW and LZW schemes. The proposed scheme attained

better PSNR, because of the effectual preprocessing and block

segmentation schemes,

1 2 3 4 50

1

2

3

4

5

6

7

Number of images

Exe

cutio

n T

ime

(s)


ILZW

LZW

Fig.13 Execution T ime Comparison among all ET C Schemes


execution time for all ETC schemes. In x-axis the number of

images is considered and for y-axis execution time is

considered. The proposed hybrid CMT with ALZW and

current methods were calculated. The proposed scheme

attained high execution time when distinguished with the

ILZW and LZW schemes.

V. CO NCLUSIO N

A hybrid CMT with ALZW based ETC scheme has been

suggested for images to enhance the security in public

network. Through median filter, the input images were pre-

processed. With the help of vertical and horizontal

segmentation, the de-noised image is segmented or

decomposed into 4 blocks. Through new block image

encryption scheme based on hybrid chaotic magic

transformation approach, encryption is executed.

Lanczos algorithm has been utilized to discover eigenvector

and eigen values in low-time complexity, in the hybrid CMT.

Pixel shuffling process is executed by shuffling the pixels

positions to the right in the clockwise directions . Hybrid CMT

has been enhanced the image security.

At last, the ALZW using IHC has been enforced for

compressing the image to a great extent with respect to CR and

PSNR. The proposed hybrid CMT with ALZW Scheme

accomplishes at the most 50 percentage compression ratios to

the Huffman coding. The hybrid CMT with ALZW Scheme

1 2 3 4 5 0

5

10

15

20

25

30

35

40

45

50

Number of images

PSNR (dB)

hybrid CMT with ALZW ILZW LZW


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also results in an algorithm with significant time, and merits

become most obvious for images with more in size. So, the

reproduced image and the original image are equal; the ALZW

Scheme is a lossless compression scheme. Enhancing the

compression ratio with the help of the new techniques is

concentrated in future work. The proposed technique can be

experimented on various varieties of data sets such as audio,

video, text as until now it is restricted to images.

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N.Mahendiran is working as Assistant Professor in

the Department of Computer Science in Sri

Ramakrishna College of Arts & Science (Formerly

SNR Sons College), Coimbatore, Tamilnadu, India. He

had an experience of 10 years and currently pursuing

Ph.D in Digital Image Processing. He has published more than 5

papers National and International journals

Dr. C. Deepa is currently working as Associate

Professor in Information Technology in Sri

Ramakrishna College of Arts & Science (Formerly

SNR Sons College), Coimbatore, Tamilnadu, India.

She has an experience of 16 Years in teaching and

research. She had published 10 papers in International journals and has

authored an book. Her area of interest are Web mining, Networks and

Communications


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Hybrid Chaotic Magic Transformation with …A s calable coding technique is suggested by Zhang et al. [12] for compression of encrypted image via a multi -resolution construction