md khairuzaman bahari - 20 journal reviews

8/3/2019 Md Khairuzaman Bahari - 20 Journal Reviews

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FAKULTI PENDIDIKAN TEKNIKAL

UNIVERSITI TUN HUSSEIN ONN MALAYSIA

BEG BERKUNCI 101, 86400, PARIT RAJA, BATU PAHAT,

JOHOR DARUL TAKZIM

CONSUMER ELECTRONIC SYSTEM (MEV 18003)

ASSIGNMENT

REVIEW ON IMAGE COMPRESSION TECHNIQUES AND ITS

APPLICATION. REVIEW AT LEAST 20 PAPERS/JOURNALS.

NAME MD KHAIRUZAMAN BIN BAHARI

I/C NO 711013-01-6353

MATRIC NO GB100142

LEARNING CENTRE POLISAS, KUANTAN

LECTURER EN. WAN SUHAIMIZAN BIN WAN ZAKI


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CHAPTER 01: INTRODUCTION

A graphics file format is the format in which graphics data is stored in a file. Graphics

file formats have come about from the need to store, organize and retrieve graphics data

in an efficient and logical way. The way a block of data is stored is usually the singlemost important factor governing the speed with which it can be read, the space it takes

up on disk and the ease with which it can be accessed by an application. A program

must save its data in a reasonable format; otherwise, it runs the risk of being considered

useless. Graphics files are an important transport mechanism that allows the interchange

of visual data between software applications and computer systems.

Traditionally, graphics refers to the production of a visual representation of a

real or imaginary object created by methods known to graphic artists, such as writing,

painting, imprinting and etching. The final result of the traditional graphics production

process eventually appears on a 2D surface, such as paper or canvas. Computer graphics

has expended the meaning of graphics to include any data intended for display on anoutput device, such as screen, printer, plotter, film decoder or videotape. In the practice

of computer graphics, creation of a work is often separate from its representation. One

way to put it is that a computer graphics process produces virtual output in memory,

from which a representation of the work can be constructed or reconstructed from

persistent graphics data save to file, possibly by the same program. An image is a visual

representation of a real-world object, captured by an artist through the use of some sort

of mechanical, electronic or photographic process. In computer graphics, the meaning of

an image has been broadened somewhat to refer to an object that appears on an output

device. Graphics data is rendered when a program draws an image on an output device.

The graphics production pipelines


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GRAPHICS DATA

Graphics data is traditionally divided into two classes: vector and bitmap. Vector data

usually refers to a means of representing lines, polygons or curves (or any object that

can be easily drawn with lines) by numerically specifying key points. Always

associated with vector data are attribute information (such as color and line thickness

information) and a set of conventions allowing a program to draw the desired objects.

Vector data

Bitmap data is formed from set numerical values specifying the colors of

individual pixels or picture elements (pels). Pixels are dots of color arranged on a

regular grid in a pattern representing the form to be displayed.

Bitmap Data

There are a number of different types of graphics file formats. Each types stores

graphics data in a different way. Bitmap, vector and metafile formats are the most

commonly used formats. However, there are other types of formats as well scene,

animation, multimedia, hybrid, hypertext, hypermedia, 3D, virtual modeling reality

language (VRML), audio, font and page description language (PDL).


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IMAGE COMPRESSION

Compression is the process used to reduce the physical size of a block of information.

By compressing graphics data, were able to fit more information in a physical size of a

block of information. Because graphics images usually require a very large amount of

storage space, compression is an important consideration for graphics file formats.

Almost every graphics file format uses some compression techniques.

There are several ways to look at compression. We can talk about differences

between physical and logical compression, symmetric and asymmetric compression and

lossless and lossy compression. The most common methods of, or algorithms for,

compression, which:

Pixel packing - Not a method of data compression per se, but an efficient way tostore data in contiguous bytes of memory. This method is used by the Macintosh

PICT format and other formats that are capable of storing multiple 1-, 2- or 4-

bits pixels per byte of memory or disk space.

Run-Length Encoding (RLE)A very common compression algorithm used by

such bitmap formats as BMP, TIFF and PCX to reduce the amount of redundant

graphics data.

Lempel-Ziv-Welch (LZW)Used by GIF and TIFF, this algorithm is also a part

of the v.42bis modem compression standard and of PostScript level 2.

CCITT EncodingA form of data compression used for facsimile transmission

and standardized by the CCITT. One particular standard is based on the keyed

compression scheme introduced by David Huffman and known widely as

Huffman Encoding.

Joint Photographic Experts Group (JPEG)A toolkit of compression methodsused particularly for continuous-tone image data and multimedia. The baseline

JPEG implementation uses an encoding scheme based on the Discrete Cosine

Transform (DCT) algorithm.

Joint Bi-Level Image Experts Group (JBIG) - A method of compressing bi-level

(two-color) image data, which is intended to replace the MR (Modified READ)

and MMR (Modified modified READ) compression algorithms used by CCITT

group 3 and Group 4.

ART - A proprietary compression algorithm developed by Johnson-Grace that

can be adapted to support audio, animation and full-motion video in the future.

FractalA mathematical process used to encode bitmaps containing a real-world image as a set of mathematical data that describes the fractal (similar,

repeating patterns) properties of the image.


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CHAPTER 2: JOURNAL REVIEW

JOURNAL #1

High Speed and Area Efficient 2D DWT Processor Based Image Compression

Sugreev Kaur and Rajesh MehraFaculty of ECE Department, National Institute of Technical Teachers Training &

Research, Chandigarh, IndiaSignal & Image Processing: An International Journal(SIPIJ) Vol.1, No.2, December 2010

With the increasing use of multimedia technologies, image compression requires higher

performance. To address needs and requirements of multimedia and internet applications, manyefficient image compression techniques, with considerably different features, have been developed.

In recent years, many studies have been made on wavelets. An excellent overview of what wavelets

have brought to the fields as diverse as biomedical applications, wireless communications, computer

graphics or turbulence. Image compression is one of the most visible applications of wavelets. In a

wavelet compression system, the entire image is transformed and compressed as a single data object

rather than block by block as in a DCT-based compression system. It allows a uniform distribution of

compression error across the entire image.

This paper presents a high speed and area efficient DWT processor based design

for Image Compression applications. In this proposed design, pipelined partially serial

architecture has been used to enhance the speed along with optimal utilization and

resources available on target FPGA. The proposed model has been designed andsimulated using Simulink and System Generator blocks, synthesized with Xilinx

Synthesis tool (XST) and implemented on Spartan 2 and 3 based XC2S100-5tq144 and

XC3S500E-4fg320 target device.

DISCRETE WAVELET TRANSFORM (DWT)

The Discrete Wavelet Transform, which is based on sub-band coding, is found to yield a

fast computation of Wavelet Transform. It is easy to implement and reduces the

computation time and resources required. The discrete wavelet transform uses filter

banks for the construction of the multiresolution time-frequency plane. The Discrete

Wavelet Transform analyzes the signal at different frequency bands with differentresolutions by decomposing the signal into an approximation and detail information.

The decomposition of the signal into different frequency bands obtained by successive

high pass, g[n] and low pass, h[n] filtering of the time domain signal.


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Figure1: Block diagram of proposed design

The combination of high pass g[n] and low pass filter h[n] comprise a pair of

analyzing filters. The output of each filter contains half the frequency content, but anequal amount of samples as the input signal. The two outputs together contain the same

frequency content as the input signal; however the amount of data is doubled. Therefore

down sampling by a factor two, denoted by 2, is applied to the outputs of the filters in

the analysis bank.

Reconstruction of the original signal is possible using the synthesis filter bank.

In the synthesis bank the signals are up sampled ( 2) and passed through the filters g[n]

and h[n]. The filters in the synthesis bank are based on the filters in the analysis bank.

Proper choice of the combination of the analyzing filters and synthesizing filters will

provide perfect reconstruction. Perfect reconstruction is defined by the output which is

generally an estimate of the input, being exactly equal to the input applied. Thedecomposition process can be iterated with successive approximations being

decomposed in return, so that one signal is broken down into many lower resolution

components. Decomposition can be performed as ones requirement.

The Two-Dimensional DWT (2D-DWT) is a multi level decomposition

technique. It converts images from spatial domain to frequency domain. One-level of

wavelet decomposition produces four filtered and sub-sampled images, referred to as

sub bands. The subband image decomposition using wavelet transform has a lot of

advantages. Generally, it profits analysis for non-stationary image signal and has high

compression rate. And its transform field is represented multiresolution like human's

visual system so that can progressively transmit data in low transmission rate line. DWTprocesses data on a variable time-frequency plane that matches progressively the lower

frequency components to coarser time resolutions and the high-frequency components to

finer time resolutions, thus achieving a multiresolution analysis. The Discrete Wavelet

Transform has become powerful tool in a wide range of applications including

image/video processing, numerical analysis and telecommunication. The advantage of

DWT over existing transforms, such as discrete Fourier transform (DFT) and DCT, is


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that the DWT performs a multiresolution analysis of a signal with localization in both

time and frequency domain.

CONCLUSION

In this paper, high speed and area efficient DWT processor based Image Compression

model has been presented. The pipelined partially serial architecture is introduced to

enhance the speed and area efficiency. The proposed design can operate at a maximumfrequency of 231 MHz by consuming of 117mW power at 28C junction temperature.

An improvement of 15% in speed has been achieved by consuming considerably less

number of resources of Spartan 3E based XC3S500E-4fg320 FPGA device to providecost effective solutions for real time image processing applications.


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JOURNAL #2

Simple Fast and Adaptive Lossless Image Compression Algorithm

Roman Starosolski, December 20, 2006Software, Practice and Experience, 2007, 37(1):65-91, DOI: 10.1002/spe.746

John Wiley & Sons, Ltd.http://www.interscience.wiley.com

This paper presents a new lossless image compression algorithm. To achieve the high

compression speed it uses a linear prediction, modified Golomb-Rice code family, and a

very fast prediction error modelling method. This paper compares the algorithm

experimentally with others for medical and natural continuous tone greyscale images of

depths of up to 16 bits.

PROPOSED METHOD

The algorithm is predictive and adaptive; it compresses continuous tone greyscale

images. The image is processed in a raster-scan order. They firstly perform prediction

using a predictor selected from affixed set of 9 simple linear predictors. Prediction errors

are reordered to obtain probability distribution expected by the data model and the

entropy coder, and then output as a sequence of residuum symbols. For encoding

residuum symbols they use a family of prefix codes based on the Golomb-Rice family.

For fast and adaptive modelling they use a simple context data model based on a

model of the FELICS algorithm and the method of reduced model update frequency.The algorithm was designed to be simple and fast. There are no methods such as

detection of smooth regions or bias cancellation being employed. Decompression is a

simple reversal of the compression process. With respect to both time and memory

complexity the algorithm is symmetric. The algorithm described herein originates from

an algorithm designed for images of 8-bit depth, which obtained high compression

speed but could not be just simply extended to higher bit depths.

The presented predictive and adaptive lossless image compression algorithm was

designed to achieve high compression speed. The prediction errors obtained using

simple linear predictor are encoded using codes adaptively selected from the modified

Golomb-Rice code family. As opposed to the unmodified Golomb-Rice codes, thisfamily limits the codeword length and allows coding of incompressible data without

expansion. Code selection is performed using a simple data model based on the model

known from FELICS algorithm. Since updating the data model, although fast as

compared to many other modelling methods, is the most complex element of the

algorithm, the reduced model update frequency method that increases the compression

speed by a couple of hundred percent at the cost of worsening the compression ratio by


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about 0.5%. This method could probably be used for improving speed of other

algorithms, in which data modelling is a considerable factor in the overall algorithm

time complexity. The memory complexity is low algorithm's data structures into

contemporary CPUs' cache memory.

EXPERIMENTAL RESULT


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The described algorithm is especially good for:

Big images, since it compresses them with the very high speed, over 60

MB/s on 3.06 GHz CPU, i.e., it needs less than 50CPU cycles per byte of

image,

Natural images of 16-bit depth, since it obtains for them very good

compression ratio - it's ratio differs by couple percent from the ratio of

the CALIC algorithm,

Noisy images, since as opposed to the other algorithms, it causes almost

no data expansion even if the image contains nothing, but noise.

CONCLUSION

Presented algorithm may improve the transmission through network when most other

algorithms are too slow. The algorithm could also be used for compressing and

decompressing, on the fly, large sets of images that are stored in memory for rapidaccess. The average compression speed on Intel Xeon 3.06 GHz CPU is 47 MB/s. For

big images the speed is over 60 MB/s, i.e., the algorithm needs less than 50CPU cycles

per byte of image. It is ideally suited for lossless compression of data to be transmitted

from modern medical and general purpose image acquisition devices, that produce

images of high bit depths, big sizes, usually containing certain amount of noise.


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JOURNAL #3

Hybrid Methods of Image Compression-Encryption

M. Benabdellah . F. Regragui . E. H. Bouyakhf.J. of Commun. & Comput. Eng. ISSN 2090-6234, Volume 1, Issue 1-2, 2011

Modern Science Publisherswww.m-sciences.com

The networks have been developing strongly these last years and became inevitable for

the modern communication. The images transmitted on these networks are particular

data because of their large quantity of information. The transmission of the images thus

raises a significant number of problems which the majority of them have no solution yet.

We enumerate for example safety, confidentiality, integrity and the authenticity of these

data during their transmission. Some medical applications require a strict associationbetween the image and its contextual data. The protection of high resolution information

(high frequencies of the images, details, scalable visualization) currently has a high

demand. This paper presents of two new methods to make images transfer safe. The

two methods are based on hybrid coding: use of compression by Faber-Schauder Multi-

Scale Transform (FMT) and encryption by the two algorithms DES and AES.

THE FABER-SCHAUDER MULTI-SCALE TRANSFORM (FMT)

The Faber-Schauder wavelet transform is a simple multi-scale transformation with many

interesting properties in image processing. In these properties, we advertise multi-scale

edge detection, preservation of pixels ranges, elimination of the constant and the linearcorrelation.

The principle of the visualization of images in the canonical base consists in

placing each coefficient at the place where its basic function reaches its maximum. The

same principle is naturally essential for the multi-scale base. The image obtained is a

coherent one which resembles an outline representation of the original image (Figure).

Indeed, the FMT transformation, like some wavelets transformation, has similarities

with the canny outlines detector, where the outlines correspond to the local maximum in

the module of transformation. In fact, in the case of the FMT transformation, on each

scale, the value of each pixel is given by the calculation of the difference with its

neighbouring of the preceding scale. Thus the areas which present a local peak for these

differences correspond to a strong luminous transition for the values of grey, while the

areas, where those differences are invalid, are associated with an area, where the level of

grey is constant.


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Figure 1: Representation on mixed-scales and on separate scales of the image Lena.

The coefficients are in the canonical base in (a) and (c) and in the Faber-Schauder multi-

scale base in (b) and (d).

Comparing the performances of the FMT transformation with the standards

method of compression, (JPEG), we will verify that we can reach good results ofcompression, without debasing the image. Those results are obtained when applying the

multi-scale transformation to the whole image, while the DCT transformation, which is

the basis of the JPEG method, is not effective when applied to reduced blocks pixels

(generally applied to blocks of size 8 8 pixels), what involves the appearance of the

blocks of artefacts on the images when the compression ratio is high. This phenomenon

of artefacts blocks is not common in the FMT transformation (Figure 2).


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Figure 2: Degradation by FMT. The percentage of eliminated coefficients:

(a) Archess original image, (b) 90%, (c) 93.

DES ALGORITHM

The Data Encryption Standard (DES) was jointly developed in 1974 by IBM and the

U.S. government (US patent 3,962,539) to set a standard that everyone could use to

securely communicate with each other. It operates on blocks of 64 bits using a secret

key that is 56 bits long. DES started out as theLucifer algorithm developed by IBM.

The US National Security Agency (NSA) made several modifications, after which it was

adopted as Federal Information Processing Standard (FIPS) standard 46-3 and ANSI

standard X3.92 [3].

This secret key encryption algorithm uses a key that is 56 bits, or seven

characters long. At the time it was believed that trying out all 72, 057, 594, 037, 927,

936 possible keys (a seven with 16 zeros) would be impossible because computers could

not possibly ever become fast enough. In 1998 the Electronic Frontier Foundation (EFF)

built a special-purpose machine that could decrypt a message by trying out all possible

keys in less than three days.

The Triple-DES variant was developed after it became clear that DES by itself

was too easy to crack. It uses three 56-bit DES keys, giving a total key length of 168

bits. Encryption using Triple-DES is simply

Encryption using DES with the first 56-bit key.

Decryption using DES with the second 56-bit key.

Encryption using DES with the third 56-bit key.

Because Triple-DES applies the DES algorithm three times (hence the name), Triple-

DES takes three times as long as standard DES. Decryption using Triple-DES is the

same as the encryption, except it is executed in reverse.


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Figure 3: Diagram of DES Encryption:Encryption of a block of the message takes place in 16 stages or rounds

AES ALGORITHM

AES is the acronym of Advanced Encryption Standard, creates by Johan Daemen and

Vincent Rijmen. It is a technique of encryption to symmetrical key. This algorithm

provides a strong encryption and was selected by the National Institute of The Standards

and Technology of the government American (NIST) like normalizes federal for the

data processing (Federal Information Processing Standard) in November 2001 (FISP-

197), then in June 2003, the American government (NSA) announced that AES wassufficiently protected to protect the information classified up to the level TOP SECRET,

which is the most level of safety defined for information which could cause

exceptionally serious damage in the event of revelations with the public.

Algorithm AES uses one the three lengths of key of coding (password)

following: 128, 192 or 256. Each size of key of encryption uses a slightly different

algorithm, thus the higher sizes of key offer not only one greater number of bits of


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jamming of the data but also an increased complexity of the algorithm. Algorithm AES

is iterative (Figure). It can be cut out in 3 blocks:

Initial Round. It is the first and the simplest of the stages. It counts only one

operation: Add Key Round.

NRounds.Nis the iteration count. This number varies according to the size of

the key used. 128 bits forN= 9, 192 bits forN= 11, 256 bits forN= 13. This

second stage consists ofNiterations comprising each one the four following

operations: Sub Bytes, Rows Shift, Mix Columns, add Key Round.

Final Round. This stage is almost identical to the one of theNiterations. The

only difference is that it does not comprise the operation Mix Columns.

Figure 4: Diagram of AES algorithm, version 128 bits.

PRINCIPLE SCHEME OF PROPOSED COMPRESSION-ENCRYPTION

The essential idea is to combine the compression and the encryption during the

procedure. General diagram is given on figure as follow: It consists in carrying out an

encryption after the stage of quantization and right before the stage of entropic coding.

To restore the starting information, one decodes initially the quantified coefficients of

the FMT matrix by the entropic decoder. Then, one deciphers them before the stage of

quantization. Lastly, one applies the IFMT (reverse FMT) to restore the image.


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Figure 5: General diagram of proposed compression encryption approaches.

CONCLUSION

The FMT transformation is distinguished by its simplicity and its performances of

seclusion of the information in the outline regions of the image. The mixed-scale

visualization of the transformed images allows putting in evidence its properties,

particularly, the possibilities of compression of the images and the improvement of the

performances of the other standard methods of compression as JPEG and GIF.

AES encryption algorithm leaves, in the stage of compression, homogeneous

zones in the high frequencies. It is approximately twice faster to calculate (in software)and approximately 1022 times surer (in theory) that DES. However, even if it is easy to

calculate, it is not enough to be taken into account in the current Wi-Fi charts. The

standard 802.11i will thus require a renewal of the material to be able to make safe the

networks of transmissions without wire. The comparison of FMT-AES method with the

methods: FMT-DES, Quadtree-AES, DCT-partial encryption and DCT-RSA, showed

well its good performance.


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JOURNAL #4

Image Compression and Water Marking Scheme Using Scalar Quantization

Kilari Veera Swamy, B.Chandra Mohan,Y.V.Bhaskar Reddy,S.Srinivas KumarThe International Journal of Next Generation Network (IJNGN), Vol. 2, No. 1, March

2010

This paper presents a new compression technique and image watermarking algorithm

based on Contourlet Transform (CT). For image compression, an energy based

quantization is used. Scalar quantization is explored for image watermarking. Double

filter bank structure is used in CT. The Laplacian Pyramid (LP) is used to capture the

point discontinuities, and then followed by a Directional Filter Bank (DFB) to link point

discontinuities. The coefficients of down sampled low pass version of LP decomposed

image are re-ordered in a pre-determined manner and prediction algorithm is used to

reduce entropy (bits/pixel). In addition, the coefficients of CT are quantized based on

the energy in the particular band.

CONTOURLET TRANSFORM (CT)

CT is considered for image compression and watermarking. For image compression,

low pass version of LP decomposed image is re-ordered and prediction algorithm is

applied. Further, all coefficients of CT are quantized using scalar quantization based on

the energy in that particular band. CT is applied to the entire image. Hence, blocking

artefacts gets reduced in CT than JPEG compressed image. If an image contains more

contours, then CT outperforms WT. The watermark image is embedded in the low passversion of LP decomposition using scalar quantization technique. CT uses double filter

bank structures for obtaining sparse expansion of typical images having smooth

contours. In this double filter bank structure, Laplacian Pyramid (LP) is used to capture

the point discontinuities, and Directional Filter Bank (DFB) is used to link these point

discontinuities into linear structures. First stage of LP decomposition and second stage

of DFB decomposition and is shown in Figure 1. One level LP decomposition is shown

in Figure 2.


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Figure 1: Contourlet Filter Bank

Figure 2: One level LP decomposition

CT gives two important properties:

Directionality. The representation should contain much more directions.

Anisotropy. To capture smooth contours in images, the representation contains

basis elements using a variety of elongated shapes.

These two properties are useful for image compression, image watermarking, and

Content Based Image Retrieval. In this method, image is decomposed using CT to

obtain different bands. Down sampled low pass version of LP decomposed image is

compressed in lossless procedure by steps, viz., topological reordering of coefficients,

scanning, prediction and calculation of residues. Finally, all coefficients of CT are

quantized.


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PROPOSED IMAGE COMPRESSION METHOD

In this method, image is decomposed using CT to obtain different bands. Down sampled

low pass version of LP decomposed image is compressed in lossless procedure by steps,

viz., topological reordering of coefficients, scanning, prediction and calculation of

residues. Finally, all coefficients of CT are quantized.

TOPOLOGICAL RE-ORDERING

The rearrangement of coefficients is based purely upon coefficient position, rather than

a function of coefficient value, hence it is designated as topological re-ordering. The

advantage of re-ordering is better accessibility of the successive coefficients for the

estimation of current coefficient value. Various re-ordering possibilities are given in an

earlier work. Re-ordering scheme used in this work is given in Figure 3.

Figure 3: Square re-ordering scheme

SCANNING, PREDICTION AND RESIDUES

Transformed image coefficients can be processed by different scanning techniques. The

raster scan consists of processing the matrix by scanning the image matrix from left to

right within each row and top to bottom in a matrix. Ideally, the value guessed for the

current coefficient P should depend on its neighbours. Notation of neighbouring

coefficients of P is shown in Figure 4. Prediction is based on one or combination of the

neighbouring coefficients. West coefficient (W) is used for estimating the current

coefficient value P. This is a simple technique when the coefficient values are uniform.

Residual is the difference between actual and estimated coefficients. Pseudo code for the

algorithm is given below to find the residual matrix for low pass version of LP

decomposed image.

NW N NE

W P E

Figure 4: Notation of neighbouring coefficients of P


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The reverse of the above procedure is considered to reconstruct the original re-

ordered matrix. Inboth the cases, if P is the first coefficient to be scanned in the

beginning of the matrix then the error at P is defined to be of same value.

PROPOSED IMAGE WATERMARKING ALGORITHM

The image watermark embedding algorithm is as follows.

The cover imagef(i,j) is decomposed into low pass image and directional

subbands by using CT decomposition.

The low pass imageflo(i,j)coefficients are quantized using following rule:z = mod(flo(i,j),Q)

Ifw(i,j) = 0 &z = Q/ 2

flo(i,j) =flo(i,j) - Q/ 2

Ifw(i,j) = 1&z >=Q/ 2

No modification inflo(i,j)

Ifw(i,j) = 1&z < Q/ 2

flo(i,j) =f lo (i,j) + Q/ 2

Here, Q is the quantization coefficient and may be selected based the

experimentation on cover image. This is usually a trial and error process. After

modifyingf(i,j) lo , inverse CT is applied with the modifiedf(i,j) lo and the

watermarked imagef(i,j)'is obtained.

The image watermarking extraction algorithm is as follows:

The watermarked imagef(i,j)' is decomposed into low pass image and

directional subbands by using CT decomposition. The watermark image is

extracted by using the following rule:

z' = mod(f lo (i,j)',Q)

Ifz < Q/ 2 , w(i,j) = 0

Ifz >=Q/ 2 , w(i,j) = 1

EXPERIMENTAL RESULTS

Experiments are performed on six grey images to verify the proposed method. These six

images are represented by 8 bits/pixel and size is 512 x 512. Images used for

experiments are shown in Figure 5.


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Figure 5: Images (Lena, mandrill, Zelda, Peppers, Airplane, Barbara)

Results in Table 1, indicating that blocking artefacts are more in JPEG than the

proposed method at the same compression ratio. PSNR values in dB are 37.0625 and

38.0159 for Lena and Zelda images for the proposed method. These values for JPEG are

36.9238 and 37.9238 dB. Almost at the same PSNR and compression ratio, the proposedmethod gives better images in terms of reduced blocking artifacts. By using WT the

Score values for Mandrill and Barbara are 9.22 and 9.07 respectively. For other image-

score values are same as CT. Mandrill and Barbara contains more contours. Hence, CT

is more effective in capturing smooth contours and geometric structures in images than

wavelet transform.

CONCLUSION

The superiority of proposed algorithm to JPEG is observed in terms of reduced blocking

artefacts. The results are also compared with wavelet transform (WT). Superiority of CT

to WT is observed when the image contains more contours. The watermark image is

embedded in the low pass image of contourlet decomposition. The watermark can be

extracted with minimum error. In terms of PSNR, the visual quality of the watermarked

image is exceptional. The proposed algorithm is robust to many image attacks and

suitable for copyright protection applications.


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JOURNAL #5

Fractal Image Coding for Emission Tomography Image Compression

Koon-Pong WongDepartment of PET and Nuclear Medicine, Royal Prince Alfred Hospital, NSW 2050,

Sydney, AustraliaBasser Department of Computer Science, The University of Sydney, NSW 2006, Sydney,

Australia0-7803-7324-3/02/$17.00 2002 IEEE

With the increasing use of teleradiology systems, large amount of data is acquired and

transmitted, thus raising the issue of medical image compression. The goal of image

compression is to represent an image with as few numbers of bits as possible while

preserving the quality required for the given application. Standard lossless compression

schemes can only yield a compression ratio of about 2:1 that is insufficient to compressvolumetric tomography image data. Common standards available in industry for lossy

image compression are usually used in non-medical applications and their application to

medical image compression is still under investigation. Fractal image coding is a new

compression technique that has received much attention recently. This study investigates

the feasibility of applying fractal image coding approach to nuclear medicine image

compression.

Quadtree-based partition scheme was used in the encoding phase to partition an

image frame into small irregular segments that after decoding process yield an

approximate image to the original. Our preliminary results show that compression ratios

higher than 10:1 can be achieved in clinical images. It was also found that there is no

diagnostic loss in the parametric images computed from the reconstructed images as

compared to those obtained from the original raw data. We conclude that fractal image

coding could be used to compress tomography images and it may be useful in

telemedicine.

FRACTAL IMAGE CODING

A fractal is a geometric form which has self-similar irregular details. The idea of fractal

image coding is based on the assumption that a large amount of self-similarity is present

in the image at the microscopic or block-image level. Thus, the image redundancies canbe exploited by means of block-based self-affine transformations. This is different from

common transform coding approaches where a single invertible transform maps the

image to a set of uncorrelated coefficients among which only the dominant ones are

retained and further processed for storage and transmission. A generic model of fractal

compression system is shown in Figure 1. In the encoding phase, an image is partitioned

into a number of disjoint blocks of size called range blocks and a number of

blocks of size called domain blocks. For each range block, the best matching


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domain block is searched in the domain pool (a collection of domain blocks) by

performing a set of transformations on the blocks so that a given metric (e.g. root mean

square) is minimised. Data compression is achieved by storing only the set of

transformations, i.e. the fractal code, which contains the complete information required

to reconstruct the image. The reconstruction (or approximation) of the original image isobtained by iterating the set of transformations on an arbitrary initial image.

Figure 1: A generic model of a fractal compression system

QUADTREE PARTITIONING

The first practical block-based fractal coding scheme was developed by Jacquin. The

weakness of this approach is that some regions of the image may not be covered well

due to the use of range blocks of fixed size. A quadtree-based fractal encoding scheme is

an extension of the Jacquins method and was used in this study. A quadtree partition is

a representation of an image as a tree in which each node corresponds to a square of the

image. Each node contains four sub-nodes, corresponding to the four identical size

quadrants of the square. The root of the tree is the original image. After some initial

numbers of partitions are performed, the squares at the nodes (i.e. range blocks) are

compared with domain blocks (which are twice the range size) in the domain pool.

The size of the domain block is shrunk by pixel averaging to match the range

size, and the affine transformation (offset and scaling) of the pixel values is found by

minimising the root mean squares (RMS) difference between the range pixel values and

the transformed domain pixel values. Apart from offset and scaling, a domain block has

eight possible isometric orientations (4 rotations and reflection with 4 rotations) to

match a given range block. Thus the domain pool can be thought of as being enlarged by

including all rotations and reflections of each domain block. All the possible domain

blocks are explored and compared with a range. If the depth of the quadtree is less than

an assumed maximum depth and if the optimal RMS difference is larger than a

threshold, the range block is further subdivided into four quadrants and the process is

then repeated until the optimal RMS is less than the threshold. The set of

transformations and domains are stored and the encoding process is completed.

Decoding process is done by iterating the set of transformations on an arbitrary

initial image and the quadtree partition is used to determine the ranges in the image. For

each range block, the size of the domain block that maps to it is shrunk by two via 2 2


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pixel averaging. The pixel values of the shrunken domain block are then placed in the

location in the range determined by the orientation information after scaling and

offsetting. Computing all the range blocks constitutes one iteration. After several

iterations, the reconstructed image will be very close to the original image.

HUMAN FDG-PET STUDY

To evaluate the performance of fractal image coding, a typical oncological FDG-PET

scan was examined. The FDG-PET scan was done in a female patient, six months after

resection of a malignant primary brain tumour in the right parieto-occipital lobe. A

partly necrotic hypermetabolic lesion was found in the right parieto-occipital lobe that

was consistent with tumour recurrence. The patient was injected with 400 MBq of FDG

and a dynamic sequence of 22 frames was acquired over 60 min on an ECAT 951R

whole-body PET tomography (CTI/Siemens, Knoxville, TN). Images were

reconstructed on a 128 128 matrix using filtered back projection with a Shepp and

Logan filter cut-off at 0.5 of the Nyquist frequency and were attenuation corrected with

a post-injection transmission method .

RESULTS AND DISCUSSION

Figure 2 shows the compression ratios for the 31 planes of dynamic images. The

maximum compression ratio in this sample is about 8:1 but the individual frame can be

compressed up to 28:1 or even higher, as shown in Figure 3. Due to noise that reduces

interpixel correlation and the tracer distribution is time-varying, there are some

fluctuations in the compression ratios. Parametric images ofKwere computed for all

planes and some representatives are shown in Figure 4. There are no appreciabledifferences between the K images computed from raw PET data (top row) and the

reconstructed data (bottom row) as all the lesions are visible. Thus, the diagnostic

information is preserved in the parametric images.


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Figure 4: Parametric images ofk(in ml/g/min) generated with raw dynamic images (top

row) and with dynamic images decoded from compressed data (bottom row) in someselected planes.

CONCLUSION

The preliminary results demonstrate the feasibility of PET image compression using

fractal image coding. The parametric images generated from the decompressed images

are visually identical to those generated from the original data without loss of diagnostic

information. As a conclusion, the fractal image compression could be used to compress

tomography images and it may be useful in PACS and telemedicine.


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JOURNAL #6

Image Cluster Compression Using Partitioned Iterated Function Systems and

Efficient Inter-Image Similarity FeaturesMatthias Kramm

Institute for Computer Science, Technical University of MunichThird International IEEE Conference on Signal-Image Technologies and Internet-Based

System978-0-7695-3122-9/08 $25.00 2008 IEEE

When dealing with large scale image archive systems, efficient data compression is

crucial for the economic storage of data. Currently, most image compression algorithms

only work on a per-picture basishowever most image databases (both private and

commercial) contain high redundancies between images, especially when a lot of imagesof the same objects, persons, locations, or made with the same camera, exist. In order to

exploit those correlations, its desirable to apply image compression not only to

individual images, but also to groups of images, in order to gain better compression rates

by exploiting inter-image redundancies. This paper proposes to employ a multi-image

fractal Partitioned Iterated Function System (PIFS) for compressing image groups and

exploiting correlations between images. In order to partition an image database into

optimal groups to be compressed with this algorithm, a number of metrics are derived

based on the normalized compression distance (NCD) of the PIFS algorithm.

THE COMPRESSION ALGORITHM

PIFS algorithms work by adaptively splitting an imageIinto a number of non-

overlapping rectangular range blocksR1 . . . Rn (using a quadtree algorithm with an

error threshold _max), and then mapping each range blockR onto a domain blockD

(withD being selected from a number of rectangular overlapping domain blocksD1, . . .

, Dm from the same image) which is scaled to the dimensions ofR by an affine

transform, resulting in a block, and is henceforth processed by a contrast scaling c and

a luminance shift l:

Rxy= Dxy+ l


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Figure 1: Cross references between PIFS compressed images of an image group

IMAGE SIMILARITY

Image databases typically consist of tens of thousands of images. The algorithm needs to

compress all images in a group as a whole2, and, more importantly, also needs todecompress the whole group in order to retrieve a single image. Hence, its desirable to

split the input data into manageable clusters. Here, the opportunity presents itself to

organize the clusters in a way that compression is optimized, i.e., that relevance is paid

to the fact which images benefit most from each other if placed into the same cluster. In

order to partition the database in such a way, a metric specifying a kind of compression

distance between to images need to be devised (so that the clustering algorithm will

know which images are similar and should be placed in the same group). Using the

normalized compression distance (NCD), this can be expressed as

with CI1,...,Inthe compressed filesize of compressing imagesI1, I2, . . . , Intogether, and

CIkthe filesize of a compression run on just a single image.


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Figure 2: NCD Image similarity based on PIFS- Images are not similar to itself under

the fractal NCD metric, if domain blocks are always larger than region blocks.

CLUSTERING OF IMAGES

It considered a number of different clustering algorithms, which all have different

advantages and disadvantages, and which will described in the following.

MST clustering: An algorithm which calculates the spanning tree from the

distance metric, and then splits the tree into clusters by cutting off edges. nCut clustering: A hierarchical method which treats the complete data set as one

big cluster, and then starts splitting the nodes into two halves until the desired

number of clusters is reached (Splitting is done by optimizing the nCut metric.

SAHN clustering: Another hierarchical method, which in each step, combines a

node (or cluster) and another node (or cluster), depending on which two

nodes/clusters have the smallest distance to each other. Distances between

clusters are evaluated using the sum over all distances between all nodes of both

clusters, divided by the number of such distances.

Relational k-Means: An extension of the classical k-Means of

multidimensional data [21], which computes centres not by the arithmetic mean,

but by finding a median node with the lowest mean distance to all other nodes.

Random clustering: Distributes nodes between clusters arbitrarily. This

algorithm was included for comparison purposes.


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CONCLUSION

In this paper, a new image cluster compression algorithm derived based on Fractal

Partitioned Iterated Function Systems (PIFS) for multi-image compression, which is

able to outperform its single-image variant considerably. This paper also presented

methods for splitting image databases into manageable groups for compression with said

algorithm. Using a feature-based metric, very large image databases can be partitionedinto manageable clusters for being compressed with the multi-image PIFS algorithm. If

the number of images is smaller and further compression efficiency is needed, the

images can be clustered using a more expensive metric, which clusters images using an

approximation of the Normalized Compression Distance (NCD) and produces better

cluster configurations, at the cost of more computing time.


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JOURNAL #7

A Pseudo Lossless Image Compression Method

Tzong-Jer Chen, Keh-Shih Chuang2010 3rd International Congress on Image and Signal Processing (CISP2010)

978-1-4244-6516-3/10/$26.00 2010 IEEE

To present a pseudo lossless compression which modifies the noise component of the bit

data to enhance the compression without affecting image quality? The hypothesis

behind the study is that the bit data contaminated by noise can be manipulated without

affecting image quality. The compression method comprises of three steps:

To estimate the noise level for each pixel,

To identify those bits contaminated by noise and replace them with zero,

To perform a lossless data compression on the processed image.

MATERIALS AND METHODS

Figure 1: The procedures of pseudo error-free image compression


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IMAGES

19 body images from computerized tomography (CT) 19 brain images from magnetic resonance (MR)

19 mammograms.

Both CT and MR data sets were from a serial 3D study. The CT images were from a GE

9800 scanner with an image size of 512512 and 12 bits deep. The MR images were

from a GE Signa 1.5 T scanner with a size of 512512. The mammographic images

were digitized from film at a size of 20482048 with an Eikonix 1412 12 bits CCD

camera. The images have been cropped to 10241024 for better storage.

ESTIMATION OF NOISE LEVEL

Standard deviation can be used to estimate the noise for the smoothed parts of the

image. Due to the difference of gray levels between objects, the standard deviations

calculated directly from original image tend to overestimate the noise levels near

boundary. Define a residual image as the subtraction between the original and its signal

component images. The signal component image is obtained by median filtering the

original image with a 3 3 window. The subtraction can effectively remove the signal

part and leave the noise part in the residual image. Thus the noise levels are the same in

both residual and original images. We can then estimate the noise level by the standard

deviation calculated from the residual image with a 5 5 window.

NOISE BITS AND ZEROED IMAGE

The number of noise bits can be estimated by simply taking the logarithm of the noise

level. Let n(x,y) be the noise level of a pixel, then the number of bits contaminated by

noise is [log2 n(x,y)], where [x] is the greatest integer function. Note that these noise

bits occupy the least significant parts in the bit data of the pixel value. Since the noise

bits do not contain any structural information, we can modify them without deteriorating

image quality. In this study, we set all the noise bits of an image to zero. Performing a

reversible compression on these images will yield better compression ratio than on the

original images.

REVERSIBLE IMAGE COMPESSION

Image compression consists of two steps: decorrelation and encoding. A common

characteristic of radiological images is that neighbouring pixels have a high degree of

correlation, which is considered redundant from the viewpoint of image information.

Redundancy in the image is first subtracted from the original image to obtain a new

image called a decorrelated image. In this study, decorrelation is performed by the


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subtraction between adjacent pixels. The subtraction is a one dimensional version of the

differential pulse code modulation (DPCM).

The encoding step is to assign a binary code word to each pixel value of the

decorrelated image. The assignment is made in such a way that, on the average, the data

rate (bits/pixel) is as close as possible to the entropy of the decorrelated image. The

arithmetic coding was used in this study. It assigns a code word to each symbol an

interval of real numbers between 0 and 1. It exploits the distribution of the image

histogram, by assigning short intervals to the most frequently occurring amplitudes and

longer intervals to the others. In theory, arithmetic coding scheme can achieve

compression bound established by information entropy.

CONCLUSION

The compression ratios are 3.10, 5.24, and 6.60 for CT, MRI, and digitized

mammograms respectively, for the new method which shows a 36.8%, 62.7% and 125%increase for the three data sets than original data. The processed images are evaluated by

two image enhancing techniques: window/level and zoom. They are indistinguishable

from original images. The proposed method demonstrates an improvement more than

40% in compression ratio than original image without deterioration in image quality.

The qualities of processed images are the same as compared with those images by lossy

JPEG2000 image compression at compression ratio around 10.


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JOURNAL #8

A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical

Trees

Amir Said, William A. PearlmanIEEE Transactions on Circuits and Systems for Video Technology, Vol6, June 1996

Presented in part at the IEEE Int. Symp. on Circuits and Systems, Chicago, IL, May 1993

Embedded zerotree wavelet (EZW) coding, introduced by J. M. Shapiro, is a very

effective and computationally simple technique for image compression. Here we offer

an alternative explanation of the principles of its operation, so that the reasons for its

excellent performance can be better understood. These principles are partial ordering by

magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and

exploitation of self-similarity across different scales of an image wavelet transform.

Moreover, we present a new and different implementation, based on set partitioning inhierarchical trees (SPIHT), which provides even better performance than our previously

reported extension of the EZW that surpassed the performance of the original EZW. The

image coding results, calculated from actual file sizes and images reconstructed by the

decoding algorithm, are either comparable to or surpass previous results obtained

through much more sophisticated and computationally complex methods. In addition,

the new coding and decoding procedures are extremely fast, and they can be made even

faster, with only small loss in performance, by omitting entropy coding of the bit stream

by arithmetic code.

SUMMARY AND CONCLUSIONS

This paper presented an algorithm that operates through set partitioning in hierarchical

trees (SPIHT) and accomplishes completely embedded coding. This SPIHT algorithm

uses the principles of partial ordering by magnitude, set partitioning by significance of

magnitudes with respect to a sequence of octavely decreasing thresholds, ordered bit

plane transmission and self-similarity across scale in an image wavelet transform. The

realization of these principles in matched coding and decoding algorithms is a new one

and is shown to be more effective than in previous implementations of EZW coding.

The image coding results in most cases surpass those reported previously on the same

images, which use much more complex algorithms and do not possess the embedded

coding property and precise rate control. It seems that the results of this codingalgorithm with its embedded code and fast execution are so impressive that it is a

serious candidate for standardization in future image compression systems.


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JOURNAL #9

Study on Huber Fractal Image Compression

Jyh-Horng Jeng, Senior Member, IEEE, Chun-Chieh Tseng, and Jer-Guang HsiehIEEE transactions on image processing, vol. 18, no. 5, may 2009

1057-7149/$25.00 2009 IEEE

In this paper, a new similarity measure for fractal image compression (FIC) is

introduced. In the proposed Huber fractal image compression (HFIC), the linear Huber

regression technique from robust statistics is embedded into the encoding procedure of

the fractal image compression. When the original image is corrupted by noises, we

argue that the fractal image compression scheme should be insensitive to those noises

presented in the corrupted image. This leads to a new concept of robust fractal image

compression. The proposed HFIC is one of our attempts toward the design of robustfractal image compression. The main disadvantage of HFIC is the high computational

cost. To overcome this drawback, particle swarm optimization (PSO) technique is

utilized to reduce the searching time.

FRACTAL IMAGE COMPRESSION

The fundamental idea of fractal image compression is the partitioned iteration function

system (PIFS) with the underlying theory founded by the fixed-point theorem and the

Collage theorem. In the coding process, we set the size of coding unit as . For encoding

an gray level image, there are non-overlapping blocks forming the range pool. Let the

contractivity of the fractal coding be a fixed value 2. Thus, the domain pool is composedof the set of overlapping blocks, each of which has size. For the case of 256X256 image

with 8X8 coding unit, the range pool contains 1024 range blocks, and the domain pool

contains 58081 domain blocks of size 16X16. For each range block in the range pool,

the fractal transformation is constructed by searching in the domain pool for the best

match.

LINEAR HUBER REGRESSOR

The least squares (LS) techniques are popular skills for linear regression due to their

elegant theoretical foundation and ease of implementation. The LS regression makes anassumption that the model has normally distributed errors. However, as many

applications involve heavier-than-Gaussian tailed distributions, the final fitted model is

influenced much by the outliers. Since most linear regressions use the I2-norm, the

hidden outliers under the samples will ruin the estimates rashly. Fortunately, robust

regression provides an alternative. Mestimation and least trimmed squares (LTS) are

two popular robust methods. Hubers method produces an M-estimator which is a

compromise between least squares and least absolute deviation (LAD) regression.


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HUBER FRACTAL IMAGE COMPRESSION

Figure 1: Flowchart of coding a range block in HFIC.

The best contrast scalingp and the brightness offset q can be obtained by

minimizing with the aid of the iteratively reweighted least squares (IRWLS)

technique. The main steps of IRWLS for the estimation of the contrast scaling and the

brightness offset for given u and v are described as follows:

Initialize and .

Evaluate the residual ei =vi - ui - for each i.

Estimate the scale parameter c.

Obtain the weights.

Compute new and .

Calculate the total cost .

If meets stopping criterion, then stop. Otherwise, go to step 2.


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PSO-BASED HUBER FRACTAL IMAGE COMPRESSION

Full search HFIC can exactly find the best domain block corresponding to each range

block, but it is very time-consuming. PSO can provide a faster way to encode the range

blocks. PSO is a population-based algorithm for searching global optimum. The original

idea of PSO is the simulation of a simplified social behaviour. It ties to artificial life,

like bird flocking or fish schooling, and has some common features of evolutionary

computation such as fitness evaluation.

Figure 2: Block Diagram of PSO

The block diagram of PSO is depicted in Figure 2 and the main steps are given as

follows.

Set the swarm size and the coefficients c1 and c2. Initialize the position and

the velocity of each particle randomly.

Set the initial individual best position and set the initial swarm best

positionz#

by current best immediately after the initialization.

Update the velocity and the position of each particle.

Evaluate the fitness value ofzj for eachj. Update if better fitness is found.

Find the new best position of the whole swarm. Updatez#if the fitness of the

new best position is better than that of the previous swarm.

If the stopping criterion is met, then stop. Otherwise, go to step 3.

CONCLUSION

Simulation results have shown that HFIC has good robustness against the outliers

caused by salt and pepper noise, but does not show significant improvement in image

quality for bell-shaped noises such as Gaussian and Laplace noises. In order to render

the proposed method practical, we have used the PSO method to speed up the encoder in

this study. The PSO method can effectively reduce the encoding time while retaining the

quality of the retrieved image.


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JOURNAL #10

High Quality document image compression with DjVu

Lon Botton, Patrick Haffner, Paul G. Howard, Patrice Simard, Yoshua Bengio and Yann LeCunAT&T Labs, Lincroft, NJ, July 13, 1998

This paper presented a new image compression technique called DjVu" that is

specifically geared towards the compression of high-resolution, high-quality images of

scanned documents in color. This enables fast transmission of document images over

low-speed connections, while faithfully reproducing the visual aspect of the document,

including color, fonts, pictures, and paper texture. The DjVu compressor separates the

text and drawings, which needs a high spatial resolution, from the pictures and

backgrounds, which are smoother and can be coded at a lower spatial resolution.

Then, several novel techniques are used to maximize the compression ratio: the

bi-level foreground image is encoded with AT&T's proposal to the new JBIG2 fax

standard, and a new wavelet-based compression method is used for the backgrounds and

pictures. Both techniques use a new adaptive binary arithmetic coder called the Z-coder.

A typical magazine page in color at 300dpi can be compressed down to between 40 to

60 KB, approximately 5 to 10 times better than JPEG for a similar level of subjective

quality. A real-time, memory efficient version of the decoder was implemented, and is

available as a plug-in for popular web browsers.

THE DJVU COMPRESSION FORMAT

The elements that compose a DjVu encoded image file are:

The text and drawings, also called the mask, are represented by a single

bitmap whose bits indicate whether the corresponding pixel in the document

image has been classified as a foreground or a background pixel. This bitmap

typically contains all the text and the high-contrast components of the

drawings. It is coded at 300dpi using an algorithm called JB2, which is a

variation of AT&T's JBIG2 fax standard.

The background is coded at 100dpi using the wavelet-based compression

algorithm called IW44.

The foreground contains the color of the text and line drawings. It generallycontains large areas of contiguous pixes with almost identical colors. This

image is coded as a low-resolution image using the same IW44 algorithm.

The last step of all compression algorithms is entropy coding. The efficiency

of IW44 and JB2 heavily draws on their use of an efficient binary adaptive

arithmetic coder called the ZP-coder [Bottou et al., 1998 ].


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OPTIMIZATION OF THE COMPRESSION RATE

DjVu achieves high compression ratios by using new compression algorithms for the

mask layer as well as for the background and foreground layers. Here are some of the

novel techniques used by DjVu: the soft pattern matching algorithm, used in the JB2 bi-

level image compression algorithm for the mask layer; the sparse set representation of

wavelet coefficients used by the IW44 wavelet-based encoder; a multi-scale successive

projections algorithm, which avoids spending bits to code the parts of the background

image that are covered by foreground objects. The new algorithms used in DjVu such as

the foreground/background/mask separation algorithm, the soft pattern matching

algorithm, and the wavelet masking technique.

RESULTS AND COMPARISONS WITH OTHER METHODS

Table 1: Test document images. The third column gives the number of documents per

category, the fourth columns reports the total file size used by the raw RGB images

Table 2: Description of the 3 DjVu sub images


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Table 3: Ratios beween the total raw file size and the total compossed file size for eachdocument category, compression format are CCITT G4, lossless and lossy JB2

CONCLUSION

DjVu is a highly efficient compression format , combined together with a browser that

enables fast internet access, can achieves compression ratios 5 to 10 times higher than

JPEG. Multi-scale bicolor clustering performs a foreground/ background/mask

separation that is more general than the standard text/image segmentation. With the soft

pattern matching algorithms, our lossy JBIG2 compression ratios are on average twice

those of lossless JBIG1, the best existing standard for bi-level images. The ZP-coder isfaster than other approximate arithmetic coders, and yields, on average, better

compression. The multi-scale successive projections algorithm for wavelet

decomposition of partially masked images significantly reduces the compressed file

sizes and can handle arbitrarily complex masks with reasonable computational

requirements. All these algorithms have been integrated into a standalone DjVu encoder.


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JOURNAL #11

JPEG-2000: A New Still Image Compression Standard

Al-Shaykh, O.K.; Moccagatta, I.; Homer Chen (1998)Conference Record of the Thirty-Second Asilomar Conference on

Digital Object Identifier: Page(s): 99 - 103 vol.1

This paper described the status and directions of the emerging ISO/IEC JPEG-2000

image compression coding standard. This new work item of the Joint Photographic

Expert Group (JPEG) 2000 is intended to create a unified coding standard for different

types of still images (bi-level, gray-level, color) with different characteristics (natural,

medical, remote sensing, text). This paper also presented an overview of the various

aspects of JPEG-2000 and highlights new functionalities supported by this emerging

standard and its potential applications.

JPEG-2000 REQUIREMENTS

JPEG-2000 attempts to achieve a digital image compression standard that would satisfy

the requirements of many applications using as limited a number of tools as possible.

Such applications include image databases, digital photography, faxing, scanning,

printing, medical imaging, remote sensing, wireless multimedia, and World Wide Web

applications. The following are the basic requirements of JPEG-2000:

Efficient coding JPEG-2000 is required to provide significantly better coding

efficiency than JPEG-baseline. Spatial and quality scalability JPEG-2000 bit stream is going to be scalable

in resolution and quality.

Complexity JPEG-2000 should be able to satisfy applications with limited

memory and processing powers.

Line and block applications JPEG-2000 should be able to compress images

that are acquired in a raster order (scanners) or decoded in line order

(printers). It should also address block based imaging.

Robustness JPEG-2000 should be robust to transmission errors.

JPEG-2000 PERFORMANCE: CODING EFFICIENCY

The current JPEG-2000 has different coding options. One coding option is for selection

between the scalar quantization (SQ) and the Trellis coded quantization (TCQ). Table 1

shows the peak signal to noise ratio (PSNR) results of the two methods for the test

imageBike in Figure 1 which was a 2048x 2560 gray levels image. In this particular

example, SQ performs better than TCQ. However, this is not always the case. For
http://ieeexplore.ieee.org.ezproxy.uthm.edu.my/search/srchabstract.jsp?tp=&arnumber=750835&openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All%26queryText%3DThe+JPEG+2000+still+image+compression+standardhttp://ieeexplore.ieee.org.ezproxy.uthm.edu.my/xpl/mostRecentIssue.jsp?punumber=6069http://ieeexplore.ieee.org.ezproxy.uthm.edu.my/xpl/mostRecentIssue.jsp?punumber=6069http://ieeexplore.ieee.org.ezproxy.uthm.edu.my/search/srchabstract.jsp?tp=&arnumber=750835&openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All%26queryText%3DThe+JPEG+2000+still+image+compression+standard


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example, some tests have shown that TCQ performs better than SQ for synthetic

aperture radar (SAR) images.

Figure 1: Test imageBike

Bit Rate

(bpp)

PSNR

TCQ SQ

0.0625 22.95 23.15

0.125025.65

25.76

0.2500 28.87 29.32

0.5000 32.86 33.32

1.0000 37.79 38.12

Table 1: Test image Bike: comparison of SQ and TCQ coding efficiency.

JPEG-2000 PERFORMANCE: ERROR ROBUSTNESS

Abit-stream packetization approach has been adopted to make JPEG-2000 robust tochannel degradation. This approach does not affect the coding efficiency and the

spatial/quality scalability features of the compression algorithm, and it only requires

minimal overhead. The bit-stream packetization approach organizes the data stream into

packets. Each packet consists of a packet header, followed by a number of encoding

units. The header contains a resynchronization marker (RM) and an encoding unit


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identification number(s). The resynchronization marker has to be uniquely decodable

from the bit-stream. The coding unit ID associates the data contained

in the current package to an absolute position in the subbandsequence hit-plane domain.

Encoder and decoder can perform resynchronization at the subbandsequence level or

bit-plane level. Table 2 shows the JPEG-2000 packetization performance. The JPEG-

2000 coder is used to compress the 2048x2056, 8-bpp gray scale imagesBike and

Woman at a rate of 0.5 bpp. Uncorrupted decoding results in a PSNR = 32.86 dB for

Bike, and PSNR = 33.29 for Woman. Trellis quantizer and the rate control implemented

in the JPEG-2000 Verification Model have been used to generate those results. In the

table, the performance is expressed in terms of average PSNR over 50 random errors.

Test Images

Corrupted Stream with Error Res.

Res Marker at

Subband Lev.

Ave. PSNR (dB)

Res Marker at

Bitplane Lev.

Ave. PSNR (dB)

Bike 14.60 17.70

Woman 19.18 21.68

Table 2: Simulation results obtained when corrupting bit-streams of test images coded at

0.5 pbb with random errors (BER 10e-4)

CONCLUSIONS

As a conclusion, JPEG-2000 attempts to achieve better compression efficiency thanJPEG-baseline and provide scalability and error resilience. Currently, the JPEG-2000

committee is working on reducing the complexity and memory requirement of the tools

accepted in the Verification Model. This may result in replacement or modifications of

some of the tools in the future. JPEG-2000 has a great potential to provide many useful

functionalities with a single standard.


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JOURNAL #12

The LOCO-I Lossless Image Compression Algorithm: Principles and

Standardization into JPEG-LSMarcelo J. Weinberger, Gadiel Seroussi, Guillermo Sapiro

IEEE Transactions On Image Processing, Vol. 9, No. 8, August 2000

LOCO-I (LOw COmplexity LOssless Compression for Images) is the algorithm at the

core of the new ISO/ITU standard for lossless and near-lossless compression of

continuous-tone images, JPEG-LS. It is conceived as a low complexity projection of

the universal context modeling paradigm, matching its modeling unit to a simple coding

unit. By combining simplicity with the compression potential of context models, the

algorithm enjoys the best of both worlds. It is based on a simple fixed context model,

which approaches the capability of the more complex universal techniques for capturing

high-order dependencies. The model is tuned for efficient performance in conjunction

with an extended family of Golomb-type codes, which are adaptively chosen, and an

embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains

compression ratios similar or superior to those obtained with state-of-the-art schemes

based on arithmetic coding. Moreover, it is within a few percentage points of the best

available compression ratios, at a much lower complexity level.

MODELING PRINCIPLES OF LOCO-I

The model of LOCO-I is shown in Figure 1. In lossless image compression schemes, the

probability assignment is generally broken into the following components:

A prediction step, in which a value xt+1

is guessed for the next sample xt+1

based on

a finite subset (a causal template) of the available past dataxt.

The determination of a contextin which xt+1

occurs. The context is, again, a function

of a (possibly different) causal template.

A probabilistic model for theprediction residual (or error signal) t+1

=xt+1

xt+1

,

conditioned on the context of xt+1


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Figure 1: Model of LOCO-I

Table 1: Compression results on new image test set (in bits/sample)

CONCLUSION

The model is tuned for efficient performance in conjunction with an extended family of

Golomb-type codes, which are adaptively chosen and an embedded alphabet extensionfor coding of low-entropy image regions.


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JOURNAL #13

Image Compression Using the Discrete Cosine Transform (DCT)

Andrew B. Watson, NASA Ames Research CenterMathematica Journal, 4(1), 1994, p. 81-88

The rapid growth of digital imaging applications, including desktop publishing,

multimedia, teleconferencing, and high definition television (HDTV) has increased the

need for effective and standardized image compression techniques. Among the

emerging standards are JPEG, for compression of still images [Wallace 1991]; MPEG,

for compression of motion video [Puri 1992]; and CCITT H.261 (also known as Px64),

for compression of video telephony and teleconferencing. All three of these standards

employ a basic technique known as the discrete cosine transform (DCT). Developed by

Ahmed, Natarajan, and Rao [1974], the DCT is a close relative of the discreteFourier transform (DFT). The discrete cosine transform (DCT) is a technique for

converting a signal into elementary frequency components. It is widely used in image

compression. Its application to image compression was pioneered by Chen and Pratt

[1984]. The goal of this paper is to illustrate the use ofMathematica in image processing

and to provide the reader with the basic tools for further exploration of this subject.

THE ONE-DIMENSIONAL DISCRETE COSINE TRANSFORM

The discrete cosine transform of a list of n real numbers s(x), x = 0, ..., n - 1, is the list of

length n given by:

Each element of the transformed list S(u) is the inner (dot) product of the input list s(x)

and a basis vector. The constant factors are chosen so that the basis vectors areorthogonal and normalized. The eight basis vectors for n = 8 are shown in Figure 1. The

DCT can be written as the product of a vector (the input list) and the nXn orthogonal

matrix whose rows are the basis vectors.


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THE TWO-DIMENSIONAL DCT

The one-dimensional DCT is useful in processing one-dimensional signals such as

speech waveforms. For analysis of two dimensional (2D) signals such as images, we

need a 2D version of the DCT. For an nXm matrix s, the 2D DCT is computed in a

simple way: The 1D DCT is applied to each row of s and then to each column of the

result. Thus, the transform of s is given by:

Since the 2D DCT can be computed by applying 1D transforms separately to the

rows and columns, we say that the 2D DCT is separable in the two dimensions. As in

the one-dimensional case, each element S(u, v) of the transform is the inner product of

the input and a basis function, but in this case, the basic functions are nXm matrices.

Each two-dimensional basis matrix is the outer product of two of the one-

dimensional basis vectors. For n = m = 8, the following expression creates an 8X8 array

of the 8X8 basis matrices.

2D BLOCKED DCT

To this point, we have defined functions to compute the DCT of a list of length n = 8

and the 2D DCT of an 8X 8 array. We have restricted our attention to this case partly for

simplicity of exposition, and partly because when it is used for image compression, the

DCT is typically restricted to this size. Rather than taking the transformation of the

image as a whole, the DCT is applied separately to 8X 8 blocks of the image. We call

this a blocked DCT. To compute a blocked DCT, we do not actually have to divide the

image into blocks. Since the 2D DCT is separable, we can partition each row into lists of

length 8, apply the DCT to them, rejoin the resulting lists, and then transpose the whole

image and repeat the process.


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CONCLUSION

DCT-based image compression relies on two techniques to reduce the data required to

represent the image. The first is quantization of the images DCT coefficients; the

second is entropy coding of the quantized coefficients. Quantization is the process of

reducing the number of possible values of a quantity, thereby reducing the number of

bits needed to represent it. Entropy coding is a technique for representing the quantized

data as compactly as possible.

Most of the computation time required transforming, quantizing, dequantizing, and

reconstruct an image is spent on forward and inverse DCT calculations. Because these

transforms are applied to blocks, the time required is proportional to the size of the

image. On a SUN Sparcstation 2, the timings increase (at a rate of 0.005 second/pixel)

from about 20 seconds for a 642 pixel image to about 320 seconds for 2562 pixels.

These times are much longer than for comparable functions written in a low-level

language such as C. However, for the purposes for which our code was developed,

namely education, algorithm development, and prototyping other applications, the

timings are acceptable.


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JOURNAL #14

Lossless Grey-scale Image Compression using Source Symbols Reduction and

Huffman CodingC. Saravanan, R. Ponalagusamy

International Journal of Image Processing (IJIP), Volume (3): Issue (5)

The image compression highly used in all applications like medical imaging, satellite

imaging, etc. The image compression helps to reduce the size of the image, so that the

compressed image could be sent over the computer network from one place to another in

short amount of time. Also, the compressed image helps to store more number of images

on the storage device.

Its well known that the Huffmans algorithm is generating minimum

redundancy codes compared to other algorithms [6-11]. The Huffman coding haseffectively used in text, image, video compression, and conferencing system such as,

JPEG, MPEG-2, MPEG-4, and H.263 etc. The Huffman coding technique collects

unique symbols from the source image and calculates its probability value for each

symbol and sorts the symbols based on its probability value. Further, from the lowest

probability value symbol to the highest probability value symbol, two symbols

combined at a time to form a binary tree. Moreover, allocates zero to the left node and

one to the right node starting from the root of the tree.

PROPOSED COMPRESSION TECHNIQUE

The number of source symbols is a key factor in achieving compression ratio. A new

compression technique proposed to reduce the number of source symbols. The sourcesymbols combined together in the same order from left to right to form a less number of

new source symbols. The average number Lavg of bits required to represent a symbol is

defined as,

where, rk is the discrete random variable for k=1,2,L with associated probabilitiespr(rk). The number of bits used to represent each value of rk is l(rk). The number of bits

required to represent an image is calculated by number of symbols multiplied by Lavg .In the Huffman coding, probability of each symbols is 0.125 and Lavg = 4.175.

In the proposed technique, probability of each symbol is 0.5 and Lavg=1.0.

TheLavg confirms that the proposed technique achieves better compression than the

Huffman Coding.


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Figure 1: Proposed Compression Technique

The different stages of newly proposed compression technique are shown in figure 1.

The source image applied by source symbols reduction technique then the outputundergoes the Huffman encoding which generates compressed image. In order to get theoriginal image, the Huffman decoding applied and an expansion of source symbols takes

place to reproduce the image.

EXPERIMENTAL RESULT

Five different test images with different redundancy developed for experiment from 0%

to 80% in step size of 20% redundancy. The Huffman coding could not be applied on

data with 100% redundancy or single source symbol. The test images with 16 rows and

16 columns will have totally 256 symbols. The images are 8 bit grey-scale and thesymbol values range from 0 to 255. To represent each symbol eight bit is required.

Therefore, size of an image becomes 256 x 8 = 2048 bit

Table 1: Huffman Coding Compression Result

Table 1 shows the different images developed for the experiment and correspondingcompression results using the regular Huffman Coding and the proposed technique. The

images are increasing in redundancy 0% to 80% from top to bottom in the table.


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Table 2: Compression Ratio versus Time

Table 2 shows the comparison between these two techniques. Compression Ratio (CR)

is defined as:

Table 3: Compression test results for chart.tif

Table 3 shows the compression result using Huffman coding, and the proposedtechnique for one of the standard image chart.tif. The proposed technique has achieved

better compressed size than the Huffman coding.

CONCLUSIONS

The present experiment reveals that the proposed technique achieves better compressionratio than the Huffman Coding. The experiment also reveals that the compression ratio

in Huffman Coding is almost close with the experimental images. Whereas, the

proposed compression technique Source Symbols Reduction and Huffman Coding

enhance the performance of the Huffman Coding. This enables us to achieve better

compression ratio compared to the Huffman coding. Further, the source symbolsreduction could be applied on any source data which uses Huffman coding to achieve

better compression ratio. Therefore, the experiment confirms that the proposedtechnique produces higher lossless compression than the Huffman Coding. Thus, the

proposed technique will be suitable for compression of text, image and video files.


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JOURNAL #15

Genetic Algorithm Applied To Fractal Image CompressionY. Chakrapani,

K. Soundara RajanARPN Journal of Engineering and Applied Sciences, Vol. 4, No. 1, February 2009 ISSN

1819-6608

In this paper the technique of Genetic Algorithm (GA) is applied for Fractal ImageCompression (FIC). With the help of this evolutionary algorithm effort is made to

reduce the search complexity of matching between range block and domain block. One

of the image compression techniques in the spatial domain is Fractal Image

Compression but the main drawback of FIC is that it involves more computational timedue to global search. In order to improve the computational time and also the acceptable

quality of the decoded image, Genetic algorithm is proposed.

GA is a search and optimisation method developed by mimicking theevolutionary principles and chromosomal processing in natural genetics. GAs aregeneral purpose optimization techniques based on principles inspired from the biological

evolution using metaphors of mechanisms such as natural selection, genetic

recombination and survival of the fittest.

FRACTAL IMAGE ENCODING

The main theory of fractal image coding is based on iterated function system, attractor

theorem and Collage theorem.

Figure 1:Domain- Range block transformations


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Figure 2:Flow Chart of Fractal Image Encoding

The basic algorithm for fractal encoding is as follows:

The image is partitioned into non overlapping range cells {Ri} which may berectangular or any other shape such as triangles.

The image is covered with a sequence of possibly overlapping domain cells. Thedomain cells occur in variety of sizes

md khairuzaman bahari - 20 journal reviews

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