a neoteric hybrid firefly algorithm and combined tree … · 2/1/2019 · carried out in the image...

Journal of Analysis and Computation (JAC) (An International Peer Reviewed Journal), www.ijaconline.com, ISSN 0973-2861

Volume XI, Issue I, Jan- December 2018

Ruhiat Sultana and Syed Abdul Sattar 1

A NEOTERIC HYBRID FIREFLY ALGORITHM AND COMBINED

TREE DATA STRUCTURE FOR THE PURSUIT OF ACCURATE

IMAGE COMPRESSION

Ruhiat Sultana 1, Syed Abdul Sattar 2 1Resaerch Scholar Rayalaseema University Kurnool Andhra Pradesh, India

2Principal Nawab Shah Alam Khan Engineering College Hyderabad Telangana, India

ABSTRACT:

This paper proposed a hybrid firefly clustering algorithm, with dual tree DS to solve the problem of low image quality, low compression ratio and high time that occurs during lossless image compression. There are three phases in our proposed approach which is done before the process of compression. They are segmentation, feature extraction and classification. The segmentation of image is done by utilizing firefly clustering algorithm whereas the feature extraction is done by texture based techniques and these features are classified by the utilization of Decision-tree classifier. After that compression and encoding is performed by making use of quad-tree. In this paper the exact feature values are extracted, classified and compressed. Therefore it provides effective compression result than previous approaches. Our proposed technique is implemented in MATLAB and therefore the experimental results proved the effectiveness of proposed image compression technique in terms of high compression ratio and low noise ratio when compared with existing techniques.

Keywords: medical imaging, information system, firefly clustering, quad-tree.

[1] INTRODUCTION

Nowadays, by considering the important advances in multimedia and networks including

telemedicine applications, the amount of information to store and transmit has dramatically

increased over the last decade. To overcome the bandwidth limitations of transmission channels

or storage systems, data compression is considered as a useful tool [1]. Image compression may

be lossy or lossless. Lossy compression is the class of data encoding methods that uses inexact

approximations and partial data discarding to represent the content. These techniques are used to

reduce data size for storage, handling, and transmitting content. Lossy compression is most

commonly used to compress multimedia data (audio, video, and images), especially in

applications such as streaming media and internet telephony [2]. By contrast, lossless

compression is typically required for text and data files, such as bank records and text articles.

Lossless compression is a class of data compression algorithms that allows the original data to

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A NEOTERIC HYBRID FIREFLY ALGORITHM AND COMBINED TREE DATA STRUCTURE

FOR THE PURSUIT OF ACCURATE IMAGE COMPRESSION


be perfectly reconstructed from the compressed data. Lossless compression is used in cases

where it is important that the original and the decompressed data be identical [3]. Compression

is preferred for archival purposes and often for medical imaging, technical drawings, clip art or

comics. The best image quality is based on better compression ratio and low noise ration.

Enhanced image quality is the main goal of image compression, however, there are other

important properties of image compression schemes: Scalability, Region of interest coding, Meta

information and processing power [4].

Images compression becomes a vital practical issue because image-based representations are

typically image intensive. The rendering techniques are classified into three major categories.

They are rendering with no geometry, rendering with implicit geometry and rendering with

explicit geometry [5]. Image compression is a critical tool that reduces the burden of storage

and transmission. The drawbacks of image compression includes the reduction of compression

rate and down grade computation time significantly. Compression work has been traditionally

carried out in the image and video communities, and many algorithms and techniques have been

proposed in the existing to achieve high compression ratios [6]. LOCO-I (LOW Complexity

Lossless Compression for Images) is an efficient compression algorithm for continuous-tone

lossless images which integrates the ease of Huffman coding with potential compression of

context models. The algorithm relied on uncomplicated static context model, which approaches

the capability of the complex universal context modeling techniques for obtaining high-order

dependencies [7]. Selective encryption and modified entropy coders with multiple statistical

models is used for doing both encryption and compression. Another approach which makes use

of multiple statistical Models is employed to transform the entropy coders into encoded format.

It is shown that security is obtained without the give up of compression performance and the

computational speed [8].

For the compression of lossless images, an effective information hiding technique was

presented. Here the information is secretly hided by the employment of index-modifying and

side-match Vector-Quantization techniques. The information which is encoded is extracted in

the decoder side [9]. A new coding scheme for transmitting the image data based on the

circumstances of cloud Gaming is designed. Results shows that this approach leads to increased

life time of the mobile battery while preserving an acceptable quality of the transmitted image

[10]. A novel lossless color image compression scheme is presented. This scheme was based on

reversible color transform (RCT) and Burrows–Wheeler compression algorithm (BWCA). The

method makes use of RCT with bi-level BWT as a result it leads in better compression by taking

advantage of the redundancy in the grey levels brought by the YUV color space [11]. Another

lossless compression technique is presented to solve the drawbacks in the real-time transmission

of aurora spectral images. This method decor relates the spatial and spectral domains bi-




dimensionally and eliminates the side information of recursively computed coefficients

effectively to obtain high quality rapid compression [12].

Due to the advent of technology, the presence of various image formats strength is provided

to image data. Due to this change in technology and the existence of different formats, high

resolution images are produced and it requires more memory for the purpose of storage. To

solve this problem a lossless technique of Image processing is introduced by taking Haar

wavelet and Vector transform techniques into consideration [13]. An innovative lossless

compression scheme has been discussed for 3D medical images. After the process of pre-

processing, the image is encoded by the utilization of embedded zero tree wavelet technique

[14]. Huge capacity and high image quality plays as prominent research contents of data hiding/

compression. An adaptive image steganography which utilizes absolute moment block

truncation coding compression (AMBTC compression) and interpolation technique (ASAI), is

presented to improve the performance of knowledge hiding scheme. As a result of this scheme a

high embedding capacity with low computational complexity and better image quality can be

achieved [15]. Another new algorithm is developed for the purpose of obtaining large capacity

image steganography. In this approach, halftoning algorithm is employed to transform the gray-

scale scanned document to binary image, which is a sparse matrix. In the next step, an algorithm

is introduced to read the halftone image, and to convert each bit-stream of the sparse matrix into

some meaningful decimal numbers, which are then to be embedded in 3-LSB bits of concealable

pixels. Concealable pixels of stego image is filtered and the quality of hidden image is preserved

by the utilization of standard deviation [16]

[2] RELATED WORK

This section provides an overview of the lossless image compression techniques available in

the literature. Several algorithms and techniques have been proposed in the last decade, but

there are considerable differences in, each with respect to the datasets used, segmentation

objectives and validation. A summary of the different approaches and their features of lossless

image compression is presented below

Nanrun zhou etal. [17] discussed about an image compression–encryption scheme to

overcome these weaknesses and reduce the possible transmission burden. This scheme was an

efficient image compression–encryption scheme which was based on hyper-chaotic system and

2D compressive sensing. Most of the existing image encryption algorithms based on low-

dimensional chaos systems bear security risks and suffer encryption data expansion when

adopting nonlinear transformation directly. To overwhelm this issue here the original image

was measured by the measurement matrices in two directions to achieve compression and

encryption simultaneously, and then the resulting image was re-encrypted by the cycle shift

operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of


A NEOTERIC HYBRID FIREFLY ALGORITHM AND COMBINED TREE DATA

STRUCTURE FOR THE PURSUIT OF ACCURATE IMAGE COMPRESSION


the pixels efficiently. The presented cryptosystem decreases the volume of data to be

transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption

system. Simulation results verify the validity and the reliability of the proposed algorithm with

acceptable compression and security performance.

Venugopal etal. [18] presented a block based lossless image compression algorithm

using Hadamard transform and Huffman encoding which was a simple algorithm with less

complexity. Medical images play a significant role in diagnosis of diseases and require a simple

and efficient compression technique. In this algorithm initially input image was decomposed by

Integer wavelet transform (IWT) and LL sub-band was transformed by lossless Hadamard

transformation (LHT) to eliminate the correlation inside the block. Further DC prediction

(DCP) was used to remove correlation between adjacent blocks. The non-LL sub-bands were

validated for Non-transformed block (NTB) based on threshold. The main significance of this

method was it proposes simple DCP, effective NTB validation and truncation. Based on the

result of NTB, encoding was done either directly or after transformation by LHT and

truncated. Finally all coefficients were encoded using Huffman encoder to compress. From the

simulation results, it was observed that the proposed algorithm yields better results in terms of

compression ratio when compared with existing lossless compression algorithms such as

JPEG2000. Most importantly the algorithm was tested with standard non-medical images and

set of medical images and provides optimum values of compression ratio and was quite

efficient.

Jinlei Zhang etal. [19] discussed about a novel distributed coding technique for

hyperspectral images. The important needs of hyperspectral images are lossless compression,

progressive transmission and low complexity onboard processing. Here the decoder produces

efficient spectral image because every individual image is compressed in slices. An adaptive

region-based prediction algorithm is designed here to eliminate spatial and spectral

redundancies of images. This technique obtained accurate compression performance and less

encoding complexity by utilizing spatial and spectral correlation simultaneously at the decoder

side.

Seyun Kim and Nam Ik Cho [20] developed a novel image compression algorithm for

lossless color images. This algorithm is based on hierarchical prediction which makes use of

upper, lower and left pixels for pixel prediction and context-adaptive arithmetic coding in

which the error was predicted using the context model and in this predicted error signal

arithmetic coding is applied. Before the process of image compression the given RGB image is

transformed to YCC image. After that grayscale image compression method is applied for the

process of encoding. This algorithm diminishes the bit rates when compared with conventional

JPEG images.

Atef masmoudi etal. [21] designed a new geometric finite mixture model-based adaptive

arithmetic coding (AAC) for lossless image compression. Applying AAC for image

compression, large compression gains can be achieved only through the use of sophisticated

models that provide more accurate probabilistic descriptions of the image. In this work, we

proposed to divide the residual image into non-overlapping blocks, and then we model the

statistics of each block by a mixture of geometric distributions of parameters estimated through

the maximum likelihood estimation using the expectation–maximization algorithm. Moreover,

a histogram tail truncation method within each predicted error block was used in order to




reduce the number of symbols in the arithmetic coding and therefore to reduce the effect of the

zero-occurrence symbols. Experimentally, we showed that using convenient block size and

number of mixture components in conjunction with the prediction technique median edge

detector, the proposed method outperforms the well-known lossless image compressors.

[3] FIREFLY-CLUSTERING WITH BI-FOLD TREE DS

The purpose of image compression is to minimize the size of image and maintain good level of

their corresponding reconstructed images. Some of the major issues occurs in image

compression is the decrease in image quality, compression ratio and increased time. To

overcome these issues hybrid firefly clustering algorithm, with dual tree DS is proposed. There

are three phases in our proposed approach. In the first phase image segmentation is done by

utilizing firefly clustering algorithm. The second phase is feature extraction, which is done by

texture based techniques and these features are classified by the utilization of Decision-tree

classifier. The third phase is the compression process which is performed by making use of

quad-tree. In the compression process encoding is performed. Encoding is used to protect

important images prior to their transmission to the recipients. Encoding and decoding is done

based on Huffman technique which makes the compression more secure.

[3.1] OPTIMAL IMAGE SEGMENTATION VIA FIREFLY CLUSTERING

ALGORITHM

The important phase in image processing is segmentation. Here the images are

segmented into several parts which contain some important information for the user. It is used

to extract information from the image. Here clustering is used to segment the image. It contains

various types of similarity pixels and consistent characteristics. It is the part of data mining

algorithm that groups the data into various number of given clusters.

In this paper we utilized a new hybrid firefly algorithm with k-means clustering for

segmentation. This firefly algorithm having two phases that is light intensity variation and

calculating the attractiveness. Here attractiveness depends on the brightness of the firefly and

the brightness in turn is defined by the objective function.

The light intensity is I(r) varies with distance ‘r’ monotonically and exponentially, is given by

(1)

Where = initial light intensity, = light absorpti on coefficient

Attractiveness is

(2)

Where = attractiveness

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The Cartesian distance between two fireflies is given by

(3)

Where , is the position of firefly In this firefly process the less bright firefly a, is

moving in the direction of the brighter firefly b. The movement is represented by

(4)

The first step of firefly algorithm is initialization of the firefly population. The size of

the firefly determines the number of solution so each firefly’s light intensity is used to calculate

its size. The distance between the fireflies is said to be Cartesian distance. The attractiveness

function is defined from the light intensity and absorption coefficient.

K-mean is used to partition the data into k clusters. Initially the centroids is selected

randomly. Then each data point is assigned to the cluster from which data point has minimum

distance. Then the data point is grouped with another nearest centroid. Then the centroid is

calculated again until convergence. In segmentation similar pixels are grouped together into

cluster. The initial centroid determines the majority of efficiency and performance of the k-

mean algorithm. When the algorithm is executed each time, the centroids are arbitrarily

generated.

This algorithm having three steps:

1. Initialization

2. Cluster assignment

3. Exploration and evaluation




Start

Initialization

Cluster assignment

Centroid update

Exploration

Termination

criteria

attained?

End

Y

N

2nd

step

1st step

3rd

step

Figure: 1 hybrid firefly clustering algorithm

Let the solution space be S which contain determinate number of fireflies , where

N is the number of fireflies. K is the number of clusters. The search space contains various

attributes and its dimension is denoted by D. Then the centroids are computed incrementally

from start to end of execution to achieve efficient centroid at each iteration. Therefore, to get

the best configuration of centroids, with given cluster with attribute have centroid

denoted by therefore the weight matrix is defined as,

(5)

The formula to estimate centroid is

(6)

The objective function is the Euclidean which is minimized. The objective function for the

firefly clustering is

(7)

The clustering matrix is defined by

(8)

When clustering matrix is enhanced, then every data point is at minimum distance from its

centroid.

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[3.2] FEATURE EXTRACTION BY TEXTURE BASED TECHNIQUES

The segmented image from the above step is processed by feature based

technique for the extraction of feature. Here Gabor wavelet filter is employed to extract the

feature [23]. This texture based technique is used to extract the feature vector from segment

region of interest. This system is similar to human visual system specifically in terms of

representation of the frequency and orientation. It is divided into many filtered image which

limited frequency and trends in intensity will change. It is considered to be suitable to

distinguish the texture. The conversion is considered as wavelet transform in which the main

wavelet is Gabor function. The segmented is given as the input to the Gaussian filter for further

extraction. After that the transformed virtual and real parts are combined. In order to improve

the size of transformed image, the texture features characterizing the image were removed. This

concept can be applied to both query image and database image. The output of the Gabor filter

is the accurately extracted features. The equation for Gabor filter is given by

pxbw

y

bw

xExpyxg

2cos*

*2*

*2),(

2

22

2

2

Where, x and y are the gray image of x and y, bw is the bandwidth value, p is the phase value.

[3.3] FEATURECLASSIFICATION USING DECISION TREE CLASSIFIER

The extracted feature is given as the input to the decision tree classifier for further

classification of the feature values. The main advantage of using decision tree is that it runs

faster to reduce time. Decision tree is a set of simple rulesand it is non-parametric because they

do not need any assumptions about the allocation of the variables in each group. In the first

step, feature is partitioned into two parts, the feature value with the highest importance is taken

into consideration. This process is continual for each subset until no more splitting is possible.

After this decision, the next feature is found then it splits the data optimally into two parts. All

non-terminal nodes contain splits. If it is followed from root to leaf node then decision tree is

the rule-based classifier. An advantage of decision tree classifiers is their simple structure

which allows for interpretation and visualization. By using the training set, decision tree is built

using objects, set of attributes and a group label. Attributes are a collection of properties

containing all the information about one object. Unlike classes, each attribute have either

ordered or unordered values, here the class is associated with the leaf and the output is obtained

from the tree. A tree misclassifies the image if the class is labelled. The proportion of images

correctly partitioned by the tree is called accuracy and the proportion of images incorrectly

partitioned by the tree is called error. Here the features extracted from the above phase are

classified for finding the optimal feature set [24].The output from the decision tree is the

optimal feature value.

[3.4] ACCURATE IMAGE COMPRESSION & ENCODING USING QUAD-

TREE AND HUFFMAN ENCODING

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The optimal feature value is finally compressed in this phase. Here both compression

and encoding process is done by using Huffman technique after the process of quad-tree

decomposition. The optimal feature value found by the decision tree is given as the input for

quad-tree. The output from the quad-tree is the compressed image. Encoding is also performed

here by means of Huffman technique which is very secured than the existing encoding

techniques. Encoding is an effective method for protecting important images before the

transmission and reception. The image is always secure no matter whether the image is stored

in the secondary storage or transmitted in networks. The quad-tree data structure is used to

represent an image.The motivation of this scheme is to have both image encoding and

compression process. The flow of the compression process is shown below.

Input valueQuad-tree

Decomposition

Huffman

encoding

CompressionHuffman

decoding

Decompressed

image

Figure: 2 Compression process

The Quad-tree approach divides the optimal feature value into four equal sized blocks,

and then various tests are contained to check that i fit meets some criterion of homogeneity. If a

block meets the criterion it is not divided any more, and the test criterion is applied to those

blocks. This process repeats iteratively until each block meets the criterion. The result may

have blocks of several different values.

The Huffman encoding algorithm begins by designing a list of all the alphabet symbols

in drizzling order of their chances. At that time it builds from the lowest up, a binary tree with a

symbol at each leaf. This is processed in steps, where two symbols with the least chances is

selected at every step, added to the top of the partial tree, deleted from the list, and replaced

with an auxiliary symbol representing the two original symbols. Once the list is reduced to

simply one auxiliary symbol (representing the entire alphabet), the tree is complete. The tree is

then traversed to recognize the code words of the symbols. Prior to the compression process

begins, the encoder needs to recognize the codes. This is done in view of the chances of

frequencies of incidence of the images. The chances or frequencies must be composed, as side

data on the output, so that any Huffman decoder can have the capacity to decompress the data.

This is basic, in such a way that the frequencies are integers and the chances can be composed

as scaled integers. It consistently adds just a couple of hundred bytes to the output. It is also

conceivable to compose the variable-length codes themselves on the output, however this may

be uncomfortable, because the codes have different sizes. It is also conceivable to compose the

Huffman tree on the output; however this may require more space than essentially the

frequencies. In any case, the decoder must comprehend what is toward the begin of the





compressed document, read it, and construct the Huffman tree for the letters in order. Begin at

the root and read the principle piece of the input (the compressed document). If it is 0, take after

the base edge of the tree; if it is 1, take after the top edge. Read the accompanying piece and

push another edge toward the leaves of the tree. Exactly when the decoder touches base at a

leaf, it finds there the main, uncompressed image, and that code is discharged by the decoder.

Steps for encoding and compression

Step 1: The value of the image is decomposed into minimum and maximum value based on the

threshold value.

Step 2: The values of x and y, mean values and block size from quad-tree decomposition is

recorded.

Step 3: Find the mean value.

Step 4: Encode the image value using Huffman technique.

Step 5: Record the coding information.

Step 6: Compress the encoded value.

Step 7: Calculate compression ratio and PSNR values.

[4].RESULTS

This section shows the performance of proposed optimization clustering algorithm with

dual tree data structure and the results obtained by them. In our proposed system the main

objective is to accurately compress the given medical image and overcome the problems that

occur during image compression.

Segmentation

During the segmentation process the given input image is segmented here using the

combination of firefly and clustering algorithm. Our medical input image is shown below.

Figure: 2 Input medical image Figure: 3 LAB image

Before the process of segmentation, there are some initial steps to be carried out. When the

input image is fetched it is transformed into Lab images whereas L represents the lightness and

a and b are the color opponents green-red and blue-yellow. The Lab color space enhances the

gamuts of RGB and CMYK color models. The input image which is transformed to Lab model




is shown below. After the color transformation of the input image, label is assigned before

carrying out the segmentation process. The labelled image is shown below.

Figure: 4 Label assumed image Figure: 5 Edge image Figure: 6 Edge

segmented image

Segmentation

The image shown below shows the computational efficiency of the firefly clustering

algorithm. At first segmentation is done using the concept of clustering technique and here due

to the occurrence of local optima problem firefly is integrates. Firefly algorithm constantly

selects the efficient centroids throughout the search space using fireflies. It also shows that

algorithm successfully overcome the local optima and achieve the global optima. The image

segment evaluation index like the standard measure of correlation coefficient is very much

effective to assess the quality of the image segmentation results. A higher value of correlation

coefficient signifies better segmentation. To quantify the conformity level between the images

after the segmentation the correlation coefficient can be used. The segmented image using

firefly-clustering is shown below.

After the process of segmentation, the next step is the feature extraction. Here texture

based features are extracted by the employment of gabor filter. The image after the process of

feature extraction classified is shown below.

Figure: 8 LAB image

COMPRESSION

During the process of compression, there are some initial steps to be carried out. At first color

transformation is to be done for the classified image obtained from the decision tree. The

transformed RGB color image is shown below. After the color transformation down sampling

should be done. Down sampling is the process of transforming the high resolution image into





small image with all the major information contained in it. Down-sampling should be done to

the blue channeled image.

Figure: 9 RGB channel

The down-sampled image is shown below.

Figure: 10 Down-sampled image Figure: 11 DCT image

Next to this the down-sampled image is organized into groups and Discrete cosine transform is

applied. Here the image is partitioned into block of pixels. After that DCT is applied to each

block. The image after applying DCT is shown below.

Finally the image is compressed by the employment of quad-tree and it is encrypted

before broadcasting. The compressed medical image is shown below which our output for the

proposed system is.

Figure: 12 Output image

[4.1] COMPARISON RESULT

To evaluate the compression result two measures are commonly applied. The first one is the PSNR (peak

signal to noise ratio) and the next is the CR (compression ratio).




PSNR is the process of measuring the quality of reconstructed image. PSNR value for accurate

compressed image must be always low. The PSNR value for our proposed system compared with the

existing FIC approach and survey is shown below.

Figure: 13 Comparison of PSNR with existing

The compression ratio for the compressed image must be always high in order to obtain good

quality image. Compression is nothing but it eliminates unwanted information in order to gain

high compression ratio. The compression ratio for our proposed system compared with the

existing FIC approach and survey is shown below.

Figure: 14 Comparison of Compression ratio with existing

[5].CONCLUSION

Our proposed scheme is used to compress the image. The objective of image compression is to

reduce irrelevance and redundancy of the image data in order to be able to store or transmit data

in an efficient form. Lossless compression is a class of data compression algorithms that allows

the original data to be perfectly reconstructed from the compressed data. . Some of the major

issues occurs in image compression is the decrease in image quality, compression ratio and

increased time. To overcome these issues hybrid firefly clustering algorithm, with dual tree DS

is proposed. In the first phase the image is segmented by utilizing firefly clustering algorithm.

In the second phase features are extracted by using texture based techniques and these features

are classified by the utilization of Decision-tree classifier. In the third phase the quad-tree data

structure is used to represent an image. In the compression process encryption is performed.

The proposed image encryption scheme is based on the principle of lossless compression. The

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proposed approach overcomes the issues that occur during the process of image compression.

Results of our proposed technique showed that it out performed when compared to the previous

work in terms of decreased PSNR value and increased compression ratio.

REFERENCES

[1] Brahimi T, Boubchir L, Fournier R and Naït-Ali A, “An improved multimodal signal-image

compression scheme with application to natural images and biomedical data”, Multimedia Tools and

Applications, Springer, pp. 1-23, 2016.

[2] Pradhan A, Pati N, Rup S and Panda AS, “A modified framework for Image compression using

Burrows-Wheeler Transform”, In Computational Intelligence and Networks (CINE), In 2nd International

Conference on IEEE, pp. 150-153, 2016.

[3] Conoscenti M, Coppola R and Magli E, “Constant SNR, rate control, and entropy coding for

predictive lossy hyperspectral image compression”, IEEE Transactions on Geoscience and Remote

Sensing, vol. 54, No. 12, pp. 7431-41, 2016.

[4] Qureshi MA and Deriche M, “A new wavelet based efficient image compression algorithm using

compressive sensing”, Multimedia Tools and Applications, Springer, vol. 75, No. 12, pp. 6737-54, 2016.

[5] Shum HY, Kang SB and Chan SC, “Survey of image-based representations and compression

techniques”, IEEE transactions on circuits and systems for video technology, vol. 13, No. 11, pp. 1020-

37, 2003.

[6] Karimi N, Samavi S, Soroushmehr SR, Shirani S and Najarian K, “Toward practical guideline for

design of image compression algorithms for biomedical applications”, Expert Systems with Applications.

Vol. 56, pp. 360-7, 2016.

[7] Weinberger MJ, Seroussi G and Sapiro G, “LOCO-I: A low complexity, context-based, lossless

image compression algorithm”, In Data Compression Conference Proceedings, IEEE, pp. 140-149, 1996.

[8] Wu CP and Kuo CC, “Design of integrated multimedia compression and encryption systems”, IEEE

Transactions on Multimedia, vol. 7, No. 5, pp. 828-39, 2005.

[9] Aishwarya KM, Ramesh R, Sobarad PM and Singh V, “Lossy image compression using SVD coding

algorithm”, In Wireless Communications, Signal Processing and Networking (WiSPNET), International


[10] Gharsallaoui R, Hamdi M and Kim TH, “Image compression with optimal traversal using wavelet

and percolation theories”, In Software, Telecommunications and Computer Networks (SoftCOM), 24th

International Conference on IEEE, pp. 1-6, 2016.

[11] Khan A and Khan A, “Lossless colour image compression using RCT for bi-level BWCA”, Signal,

Image and Video Processing. Vol. 10, No. 3, pp. 601-7, 2016.

[12] Kong W, Wu J, Hu Z, Anisetti M, Damiani E and Jeon G, “Lossless compression for aurora spectral

images using fast online bi-dimensional decorrelation method”, Information Sciences, vol. 381, pp. 33-

45, 2017.




[13] Sikka N, Singla S and Singh GP, “Lossless image compression technique using Haar wavelet and

vector transform”, In Research Advances in Integrated Navigation Systems (RAINS), International


[14] Rajakumar K and Arivoli T, “Lossy Image Compression Using Multiwavelet Transform for

Wireless Transmission”, Wireless Personal Communications, vol. 87, No. 2, pp. 315-33, 2016.

[15] Tang M, Zeng S, Chen X, Hu J and Du Y, “An adaptive image steganography using AMBTC

compression and interpolation technique”, Optik-International Journal for Light and Electron Optics, vol.

127, No. 1, pp. 471-7, 2016.

[16] Soleymani SH and Taherinia AH, “High capacity image steganography on sparse message of

scanned document image (SMSDI)”, Multimedia Tools and Applications, pp. 1-21, 2016.

[17] Zhou N, Pan S, Cheng S and Zhou Z, “Image compression–encryption scheme based on hyper-

chaotic system and 2D compressive sensing”, Optics & Laser Technology, Elsevier, vol. 82, pp. 121-33,

2016.

[18] Venugopal D, Mohan S and Raja S, “An efficient block based lossless compression of medical

images”, Optik-International Journal for Light and Electron Optics, vol. 127, No. 2, pp. 754-8, 2016.

[19] Chaurasia V and Chaurasia V, “Statistical feature extraction based technique for fast fractal image

compression”, Journal of Visual Communication and Image Representation, vol. 41, pp. 87-95, 2016.

[20] Zhao D, Zhu S and Wang F, “Lossy hyperspectral image compression based on intra-band

prediction and inter-band fractal encoding”, Computers & Electrical Engineering, Elsevier, Vol. 54, pp.

494-505, 2016.

[21] Masmoudi A, Chaoui S and Masmoudi A, “A finite mixture model of geometric distributions for

lossless image compression”, Signal, Image and Video Processing. Vol. 10, No. 4, pp. 671-8, 2016.

[22] Chang HK and Liu JL, “A linear quadtree compression scheme for image encryption”, Signal

Processing Image Communication. Vol. 10, No. 4, pp. 279-90, Sep 1, 1997.

[23] Tallapragada VS, Reddy DM, Kiran PS and Reddy DV, “A Novel Medical Image Segmentation and

Classification using Combined Feature Set and Decision Tree Classifier”, International Journal of

Research in Engineering and Technology.Vol. 4, No. 9, pp. 83-6, 2016.

[24] Sharma A and Sehgal S, “Image segmentation using firefly algorithm”, InInformation Technology

(InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect your Worlds,

International Conference. IEEE. pp. 99-102, Oct 6, 2016

[25] Sundararaj GK and Balamurugan V, “An expert system based on texture features and decision tree

classifier for diagnosis of tumor in brain MR images”, InContemporary Computing and Informatics

(IC3I, International Conference. IEEE. pp. 1340-1344, Nov 27, 2014.

[26] Ergen B and Baykara M, “Texture based feature extraction methods for content based medical

image retrieval systems”, Bio-medical materials and engineering. Vol.24, No. 6, pp. 3055-62, Jan 1,

2014.


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