Review of Diverse Techniques Used for Effective Fractal Image Compression

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
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Review Of Diverse Techniques Used For Effective Fractal Image
Compression
Shephali D. Wakalkar1, Dr. P. R. Rothe2, Prof. Mr. C. N. Bhoyar3
1 M.Tech Student, Department of Electronics Engineering, P.C.E, Nagpur, Maharashtra, India
2 Assistant Professor, Department of Electronics Engineering, P.C.E, Nagpur, Maharashtra, India
3 Assistant Professor, Department of Electronics Engineering, P.C.E, Nagpur, Maharashtra, India
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Abstract - The image compression in an image processing
plays an important role since the beginning of Internet era
and telecommunication. It is necessary for efficient storage
and transmission of image. Fractalimagecompression(FIC)is
one of the most suitable image compression approachesfor its
high compression ratio and quality of retrieved images. Many
algorithms are available to compress an image file like Quad
tree Partitioning Huffman Coding (QPHC), Discrete Cosine
Transform based FIC (DCTFIC), Discrete Wavelet Transform
based FIC (DWTFIC), Grover’s quantum search algorithm
based FIC (QAFIC)and Tiny Block Size Processing
algorithm(TiBS). This paper presents different approach of
designing a fractal image compressioninordertoenhancethe
compression ratio with low losses in the image.
Key Words: Image Processing,DiscreteCosineTransform
(DCT), Discrete Wavelet Transform (DWT), FractalImage
Compression (FIC), Grover’s Quantum Search Algorithm
(QSA), Tiny Block-Size Processing Algorithm (TiBS).
1. INTRODUCTION
Images are very useful documentsnowadaysfora numberof
applications. They need to be compressed before storing
and transmitting, due to limited bandwidth and storage
capacity. Image compression plays an important role in
multimedia and digital communicationfields. Thepurposeof
image compression is to reduce irrelevanceandredundancy
of the image data in an efficient form. This not only reduces
the storage cost but also increasesthespeedoftransmission.
Image compression is divided into two categories which are
Lossy as well as Lossless [1]. In lossless compression, the
reconstructed image after compression is numerically same
as the original image. Thus, it gives good quality of
compressed images, but yields only less compression. In
lossy compression [2], the reconstructed image contains
some degradation comparative to the original due to loss of
data with higher compression ratio. For lossless image
compression, various approaches available are Variable-
Length encoding, AdaptivedictionaryalgorithmssuchasBit-
plane coding , LZW coding, lossless predictive coding, etc.
For lossy compression, various approaches are lossy
predictive coding and transform coding such as Discrete
Cosine Transform (DCT) and Discrete Wavelet Transform
(DWT) [3].
Fractal image compression (FIC) was firstly proposed by
Arnaud E. Jacquin [4]. It is one of the lossy compression
technique with high Compression ratio and fast
decompression times. The decoding phase is independent of
the reconstructed image and the reconstructed image is of
good quality [5]. FIC is based on fractal geometry thatmeans
split geometric shapes that can be break into parts, each of
which is a decreased-size copy of the total, a property called
self-similarity [6]. FIC is good for natural images and
textures because they exhibits enormous amount of self-
similarities. So there is huge work load of searching self-
similarities, which lead to FIC rapid development.
• In recent years, many FIC algorithms have been proposed,
such as discrete cosine transform based FIC (DCT-FIC) [7],
Discrete wavelet transform based FIC (DWT-FIC) [8],
Baseline FIC etc. But reducing intrinsic computational
complexity of FIC is still a problem. Fortunately, L. Grover
[9]–[11] invented Grover’s quantum search algorithm
(QSA), based on quantum computing. The idea of quantum
computing is brought intoFIC,toutilizequantumparticlesas
a computational resource in order to reduce search
complexity in FIC. C. Zalka [12] proved that Grover’s QSA is
precisely best in search problems. This Grover’s quantum
search algorithm based FIC (QAFIC) reduces the time
complexity of FIC drastically and maintain quality of
retrieved images without sacrificing the compression ratio.
For the above reasons and motivations, in this research, we
try to use this QAFIC algorithm for further improvement.
2. LITERATURE REVIEW
The research papers on the design of fractal image
compression are published in different journals and
presented in many conferences.
Utpal Nandi and Jyotsna Kumar Mandal et. al.[13] designed
an image compression based on the new fast classification
scheme with quadtree partitioning method. In this method,
the quadtree partitioning scheme where a range is broken
up into four equal sized sub-ranges and the classification
scheme divides square block of image (range/domain) into
16 sub-block. For each block, a 64 bit ID is generated. The ID
has row part and column part each of 32 bits. The row part
has four 8 bit sub-ids- ID1, ID2, ID3 and ID4. To generate ID
for each row, each sub-block are assigned a two bit code out
of four possible codes 00, 01,10 and 11 that are termed as

Volume: 04 Issue: 05 | May -2017 www.irjet.net p-ISSN: 2395-0072
row code (RC). Similarly, to generate ID for each column,
each sub-block are assigned a two bit code out of four
possible codes 00, 01, 10 and 11 that are termed as column
code (CC). This classification scheme reduces the
compression time as compared to the other image
compression techniques, also maintain the same
compression ratio and peak signal to noise ratio (PSNR).
Chong Fu and Zhi-liang Zhu [14] designed a new block
classification method based on the edge characteristic of an
image block. There are total three steps for the functioning
of Discrete Cosine Transform based fractal image
compression (DCT-FIC). First one is image partition in
which image is partitioned into non overlapping a set of
pixels range blocks and overlapping set of pixels domain
blocks. Second one is image block classification inwhich the
range and domain blocks are divided into three classes
based on their DCT lower frequency coefficients and third
one is best match exploiting in which onlythedomain blocks
match with the range block are calculated. The classification
is based on the lower frequency horizontal and vertical DCT
coefficients of an image block. This method considerably
improves the fractal encoding speed and also satisfied the
fidelity of the reconstructed image.
Padmavati. S and Dr. Vaibhar Mesharam [15] designed an
image compression on hybrid methodology. In this
methodology, the lossy and lossless compression methods
are combined. Firstly, the given image is compressed using
DCT and compression on similar blocks of the image is
avoided by fractal quadtree image compression. Finally the
image is encoded effectively by using Huffman encoding
which improves the quality of the compressed image. The
combination of DCT and fractal quadtreedecomposition was
successful in terms of reducing the encoding time and
maintaining the quality of the image. This technique is also
applied to many real time applications such as medical
images, satellite images, etc. There is also improvement in
compression ratio as compared to the normal fractal
compression using quadtree decomposition of image.
Mehdi MasoudiChelehgahiandMohsenDerakhshanNia [16]
designed an image compression based on high speed
intelligent classification algorithm using DCT coefficients.
This method is particularly designed to reducethe encoding
time. In this method, it reshape the given image into 1D
array and calculate the DCT and standard deviation of each
row.
Ahmad A. Nashat and N. M. Hussain Hassan [17] designed an
image compression based upon the Wavelet Transform and
the Statistical Threshold. This method is based on the Haar
Wavelet transform. The Discrete Wavelet Transform(DWT)
of the image is generated by obtaining wavelet
decomposition coefficients for the desired levels. The
histogram for the selected level is calculated anda threshold
for the decomposed image coefficient is selected which is
based upon the statistics of the histogram. Then wavelets
combines with fractal image in order to get the best results
for image compression and decompression. This fractal
image compression with wavelet transform can effectively
solve the noise problem.
Cristian Duran-Faundez, Vincent Lecuire, Francis Lepage
were proposed the Tiny block-size coding [18], for energy-
efficient image compression andcommunication.TinyBlock-
Size algorithm (TiBS) is a lossy compression algorithm. It
wiil enhance the compression ratio and also reduces the
effects which might occur due to the lossy nature of normal
Fractal Image Compression (FIC). Since DCT or DWT is
computationally intensive, the Encoder in TiBs does not use
DCT or DWT. This algorithm operates on blocks of 2x2
pixels. Each block is encoded independently, based on three
stages: uniform scalar quantization, self-adaptive pixel
removal, and variable-length coding.
3. CONCLUSIONS
The study of papers shows differentapproachesofdesigning
the fractal image compression. Fractal Image Compression
has been implemented using Quadtree Partitioning with
Huffman Coding (QPHC) algorithm,DCT basedFractal Image
Compression Algorithm (DCT-FIC), DWT based Fractal
Image Compression Algorithm (DWT-FIC), Tiny Block Size
Processing based Image CompressionAlgorithm(TiBS) and
Grover’s Quantum Search Algorithm based Fractal Image
Compression (QAFIC). Especially, QAFIC performs betterfor
the images that consist of detailed view and structural
similarities. It reduces the intrinsic computational
complexity and maintain the quality of retrieved images
without sacrificing compression ratio. A new approach can
be proposed for image compression where QAFIC is
combined with TiBS which shall enhance the compression
ratio with low losses in the image.
REFERENCES
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Review of Diverse Techniques Used for Effective Fractal Image Compression