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ISSN-2349-1841(Online)
Volume 1, Issue 1, January 2015
International Journal of Research Development
& Innovation (IJRDI)
Research Paper
Available online at:www.ijrdi.com
Review on:-Image Encryption and Compression
using HAAR and COIFLET Wavelet Transform
Navita Palta#1, Ms. Neha Sharma#2
#
Mtech Student, CEC LANDRAN, PTU
1
navitapalta@gmail.com
2
Assistant Professor
CEC LANDRAN, Punjab Technical University
2
er.nehasharma09@yahoo.com
Abstract: There are several ways of encrypting an image.
In this research, HAAR and COIFLET Wavelet is
applied with Data Encryption algorithm to encrypt the
full image in a secure manner, after encryption the
original file is compressed and result will be compressed
image. The image encryption technique works in
prediction error domain which provides high extent of
safety[1]. Many Image Compression Techniques have
been proposed earlier but they were not secure enough
and compression ratio is poor. Lossless Encrypted
compression technique is used in our proposed work.
Therefore, HAAR and COIFLET Wavelet transform is
used with encryption and compression system, to get
better compression efficiency.
Keywords: Encryption,
COIFLET Wavelet.
Compression,
HAAR
Image encryption: The process of converting an image into
unreadable format so that it can be transmitted over the
network safely[5].The pixels of Leena.jpg image is in
readable format means one can easily recognize the image
so to convert it into unreadable format. . The size of
encrypted image remains same as that of original image.
Several ways are there to encrypt an image which helps to
secure an image send over the network[18].
and
I. INTRODUCTION
Due to advancement of multimedia and network
technologies, security of multimedia content becomes more
important[10]. This security protects the digital videos and
images. Digital images are used in different areas like
military, geographical areas, defence, hospitals etc. This
information is collected and stored in computers in the file
form and transmitted over the network. Many intruders are
available across the network that can break the security and
hinders the confidentiality which is the legal requirement of
images[18].Encryption techniques fulfil the security
requirements for different multimedia applications. Since
encrypted file contain large amount of data so, we
compress it and obtain the compressed file of it.
Compressed file requires less storage space and transfer
speed is much more than uncompressed file. Therefore, the
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main purpose is to compress the file to such an extent such
that quality of images remains good.
Figure 1. Encryption of an image.
Kinds of Encryption: There are two types of encryption
namely Symmetric and Public key encryption.
1.
Symmetric Key Encryption: In this scheme, both
encryption and decryption keys are same
therefore, both sender and receiver side have same
key before starting communication[6].
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International Journal of Research Development & Innovation (IJRDI)
Volume 1, Issue 1, January 2015, Pg. 45-49
obtained by removing one or more out of three data
redundancies namely:
Secret Key
Cipher text
Cipher text
Channel
Encryption
Decryption
Channel
Plain Text
Plain Text
Figure 2. Symmetric key Encryption
2.
Public Key Encryption: Here encryption key is
known to everyone whereas decryption key is
known to receiver side that allows them to read
the encrypted message.
Sender’s public
Key
Recevier’s public
Key
Ciphertext
Encryption



Coding Redundancy
Interpixel Redundancy
Psychovisual Redundancy
Coding redundancy: When optimal code words are less
than required words then code is said to be redundant.
Interpixel Redundancy: Correlation between the pixels of
image is called as Interpixel redundancy.
Psychovisual redundancy: It is due to data that is ignored
by the human visual system.
After the encryption of image, encrypted image is
transformed into compressed image. However the pixel
size remains the same but there is change in storage size.
For human eye there is no difference in outlook of these
two images but there is lots of difference in their pixel
values and storage size[13].
Cipher text
Channel
Decryption
n
Plain Text
Plain Text
Figure 3. Public Key Encryption
It is also known as asymmetric key cryptography[6]. Pair
of keys are used for encryption and decryption in this type
of encryption. It is computationally easy for a user to
generate their own public and private key-pair. Public-key
cryptography is used as a method of assuring the
confidentiality and authenticity.
Figure 4. Compression of an image.
Block diagram of Image compression: The following
diagram shows the steps of how compression is performed.
It consist of five stages namely mapper, quantization,
symbol code, symbol decoder and inverse mapper.
f(x,y)
Mapper
Uses of Encryption:
 Encryption allows secret communication.
 It is used to protect the data in transit.
 It protects the confidentiality of messages[18].
Applications of Image Encryption:
 We can apply Image encryption to different types of
protocols:
o Message oriented
o Transaction oriented
o Session oriented
 Stenography
 Digital Watermarking
Image Compression: The process used to compact the
image
representation,
thereby
the
image
storage/transmission requirements[9]. Compression is
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Symbol
Decoder
Quantizer
Symbol
coder
Inverse
Mapper
compressed
Image
F(x,y)
Figure 5. Block Diagram of image compression[8].
The encoder is used for removing the coding, Interpixel
and Psychovisual redundancies of input image. In first step,
the mapper coverts the input image into a format to
decrease the Interpixel redundancies. The second step,
qunatizer block decreases the accuracy of previous output
in accordance with a predefined criterion. In third and final
step, a symbol decoder makes a code for quantizer output
and maps the output in accordance with the code. In
backward direction , there is the inverse operations of the
encoder’s symbol coder and mapper block. As quantization
is not reversible, an inverse quantization is not present.
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International Journal of Research Development & Innovation (IJRDI)
Volume 1, Issue 1, January 2015, Pg. 45-49
Image Compression Techniques: There are two categories
of compression techniques classified as:
1. Lossless
Compression
Techniques:
The
compression which does not add noise to an image
and original image is recovered from its
compressed image[14].Following techniques are
included in lossless compression[8]:
 Run length encoding
 Huffman encoding
 COIFLET
 HAAR
 LZW Coding
2. Lossy Compression Technique: By this scheme,
the decompressed image is not same as that of the
original image, but reasonably close to
it[12].Lossy compression techniques includes
following schemes[8]:
 Transformation Coding
 Vector Quantization
 Fractal Coding
 Block Truncation Coding
 Subband Coding
Benefits of Compression:
 It provides a potential cost savings associated with
sending less data over switched telephone network
where cost of call is really usually based upon its
duration.
 It not only reduces storage requirements but also
overall execution time.
 It also reduces the probability of transmission
errors since fewer bits are transferred.
 It also provides great level of security against
monitoring.
II. LITERATURE REVIEW
Jiantao Zhou et al. 2014 proposed designing an efficient
image encryption-then-compression system via prediction
error clustering & random permutation. In the proposed
framework, the image encryption had achieved by
prediction error clustering and random permutation. The
compression of the encrypted data is then achieved by a
context-adaptive arithmetic coding approach[1].
R. Mehala et al. 2013 proposed a new image compression
algorithm using Haar Wavelet Transformation. In that
paper, 8x8 transform matrix was able to be obtained by
appropriately inserting some 0’s and ½’s into the Haar
Wavelet. The basis of the Haar Wavelet algorithm was
based on integers and made sufficiently sparse orthogonal
transform matrix. A Haar Wavelet algorithm was
developed for fast computation[2].
J. Zhou et al. 2012 proposed l2 restoration of l∞-decoded
images via soft-decision estimation. In that paper, a
new soft decoding approach was developed to reduce
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the l2 distortion of l∞-decoded images and retain the
advantages of both min-max and least-square
approximations. The new soft decoding technique was able
to even outperform JPEG 2000 for bit rates higher than
1bpp, a critical rate region for applications of nearlossless image compression. All the coding gains were
made without increasing the encoder complexity as the
heavy computations to gain coding efficiency were
delegated to the decoder[4].
Nidhi Sethi et al. 2011proposed Image Compression using
Haar Wavelet Transform. In that paper, Haar Wavelet
Transform was implemented. The results in terms of PSNR
and MSE show that the Haar transformation was able to be
used for image compression. The quantization was done by
dividing the image matrix into blocks and taking mean of
the pixel in the given block. It was clear that DWT had
potential application in the compression problem[7].
III. DIFFERENTCOMPRESSION TECHNIQUES
Following are the different types of compression technique:
Haar wavelet: The HAAR Wavelet is the sequence of
functions. This sequence was introduced in1909 by Alfred
Haar[2].Wavelets are mathematical functions that were
developed by scientists working in several different fields
for the purpose of sorting data by frequency. Data that is
translated is matched with its scale after getting sorted at a
resolution. Data is studied at different levels that helps for
the development of a more complete picture. Every small
and large features are studied separately. The wavelet
transform is not Fourier-based and therefore wavelets do a
better job of handling the data which are discontinued.
The Haar wavelet works on data after calculating the sums
and differences of elements which are adjacent to each
other[2]. The Haar wavelet works first on adjacent
horizontal elements and secondly on adjacent vertical
elements. The Haar transform is calculated by:
1/√2 [1
1]
1 −1
Following are the properties of Haar transform:
 No need for multiplications. It requires only
additions and there are many elements with zero
value in the Haar matrix, so the computation time
is short. It is faster than Walsh transform, whose
matrix is composed of +1 and −1.
 Input and output length are the same. However,
the length should be a power of 2, i.e. N=2k ,K
€N.3. It can be used to analyse the localized
feature of signals. Due to the orthogonal property
of the Haar function, the frequency components of
input signal can be analyzed.
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International Journal of Research Development & Innovation (IJRDI)
Volume 1, Issue 1, January 2015, Pg. 45-49
Advantages of Haar Wavelet:
 It is the simplest possible wavelet.
 It is not differentiable so, can be used for the
analysis of signals with sudden transitions, such a
monitoring of tool failure in machines.
subjective test with specified procedures[8]. The PSNR
between input and compressed image can be obtained using
following formula:
COIFLET Wavelet: Coiflets are the wavelets designed by
Ingrid Daubechies. These are the discrete wavelets which
are made at the request of Ronald Coifman for having
scaling functions along with vanishing moments . The
wavelets are symmetric in nature and its function have N/3
vanishing moments and scaling functions N/3-1 which are
used in different applications with the help of CalderonZygmund Operators.
Block Diagram of Proposed system: The following
diagram depicts the steps of proposed work in which the
encryption and compression are the two main tasks.
The normalization of both scaling function (low-pass filter)
and the wavelet function (High-Pass Filter) is done by a
factor1 − √2. There are some coefficients for the scaling
functions for C6-30. The wavelet coefficients are obtained
by reversing the order of the scaling function coefficients
and then reversing the sign of every second.
Mathematically, this looks like BK= (-1)KCN-1-Kwhere k is
the coefficient index; B is a wavelet coefficient and C is a
scaling function coefficient. N is the wavelet index, i.e 6
for C6.The 2N moments of wavelet functions are equal to 0
and the 2N-1 moments of scaling functions are equal to 0.
The two functions have a support of length 6N-1[3]. F=
coifwavf(W) returns the scaling filter associated with the
Coiflet wavelet specified by the string W where W =
'coifN' whereas the values of N are 1, 2, 3, 4 or 5.
Advantages of Coiflet Wavelet:
 These wavelets are symmetric in nature.
 Coiflet wavelets are same as that of Daubechies
wavelet[8].
IV. PARAMETERS USED
There are many parameters given which are used in
previous research papers.
MSE: Mean Squared Error is essentially an image fidelity
measure. The goal of an image fidelity measure is to
compare two images by providing a quantitative score that
describes the degree of difference and errors between
them[8]. The MSE between two images is given by the
following formula:
MSE = (1/N)Σi|x(i)- e(i)|2
Here x and e are the input and compressed image
respectively and N is the size of image.
PSNR: Embedding this extra data must not degrade
human perception about the object. Evaluation of
imperceptibility is usually based on an objective measure
of quality, called peak signal to noise ratio (PSNR), or a
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PSNR=20log10(PIXEL_VALUE/MSE)
Start
Original Image
Encryption
Compression
Analysis and
comparison
Figure 6. Block Diagram of Proposed System
Following are the steps used to make an efficient image
encryption and compression system.
STEP 1: Take an input original image.
STEP 2: Perform encryption process on it in order to
convert it into unreadable format.
STEP3: Finally Haar and COIFLET wavelet transform with
encryption algorithm are applied on the input image.
STEP 4: Compare Obtained results.
V. CONCLUSION
In previous designed image encryption and compression
system, the quality of obtained compressed image is not too
good. And the compression techniques used in earlier
proposed system were not secure enough and compression
ratio is poor. In this paper, two compression techniques
named HAAR and COIFLET wavelet transform are
combined for compressing an image with Encryption and
then result will be compared with previous Compression
Technique i.e., Adaptive Arithmetic Coding on the basis of
Compression ratio, PSNR, MSE, Error rate. By using this
two tier compression, compressed image can not get
distorted and compression ratio will also improved thus our
purpose will fulfilled. Thus, quality of image can be
improved by using this two tier system and results will be
good.
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ACKNOWLEDGMENT
Thanks to my Guide and family member who always
support, help and guide my during mu dissertation. Special
thanks to my father who always support my innovative
ideas.
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