T4408103107

Ankita Hundet et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 7), April 2014, pp.103-107
www.ijera.com 103 | P a g e
Survey for Image Representation Using Block Compressive
Sensing For Compression Applications
Ankita Hundet, Dr. R.C. Jain, Vivek Sharma
Department of Information Technology SATI Vidisha, India
Abstract
Compressing sensing theory have been favourable in evolving data compression techniques, though it was put
forward with objective to achieve dimension reduced sampling for saving data sampling cost. In this paper two
sampling methods are explored for block CS (BCS) with discrete cosine transform (DCT) based image
representation for compression applications - (a) coefficient random permutation (b) adaptive sampling. CRP
method has the potency to balance the sparsity of sampled vectors in DCT field of image, and then in improving
the CS sampling efficiency. To attain AS we design an adaptive measurement matrix used in CS based on the
energy distribution characteristics of image in DCT domain, which has a good impact in magnifying the CS
performance. It has been revealed in our experimental results that our proposed methods are efficacious in
reducing the dimension of the BCS-based image representation and/or improving the recovered image quality.
The planned BCS based image representation scheme could be an efficient alternative for applications of
encrypted image compression and/or robust image compression.
Keywords—Block compressive sensing, Coefficient random permutation, compressive sensing, random
sampling, Image representation,
I. INTRODUCTION
Recent years have seen important interest in
the paradigm of compressed sensing (CS) which
permit, in certain conditions, signals to be sampled at
sub-Nyquist rates via linear projection onto a random
basis while still enabling exact reconstruction of the
original signal. As applied to 2D images, though, CS
faces some challenges including a computationally
expensive reconstruction process and huge memory
required to store the random sampling operator. in
recent times, numerous fast algorithms have been
developed for CS rebuilding, while the latter
challenge was addressed in [1] using a block-based
sampling operation. Additionally in projection- based
Landweber iterations were planned to achieve fast CS
reconstruction while simultaneously imposing
smoothing with the goal of improving the
reconstructed-image quality by eliminating blocking
artifacts. Sparse representation and compressive
sensing establishes a more rigorous mathematical
framework for studying high-dimensional data and
ways to discover the structures of the data, giving rise
to a large range of efficient algorithms. When
transmitting data over insecure bandwidth-limited
channels, data compression and encryption is for all
time needed. An encryption algorithm converts the
data from comprehensible to incomprehensible
structure, thus making the encrypted data difficult to
compress using any of the classical compression
algorithms, which relies on intelligence embedded in
the data. Hence, traditionally the encryption always
follows compression. While such a scheme is suitable
for most of the applications, there are some
applications which need encryption to be carried out
before compression [2]. Consider for example, an
information owner and a network operator who does
not trust each other. In such a case, to protect his
content, the information owner encrypts his data
before giving it to network operator. Due to the
bandwidth limitation the network operator is forced
to compress this encrypted data stream [3]-[5].
II. PROBLEM FORMULATION
The idea of CS is to represent the signal by
non adaptive random projections to reduce the
sampling rate, which is considered as an advantage of
CS. However, the main challenges existed in CS for
practical applications include how to reduce
efficiently measurement rate with preserving good
recovered image quality and decreasing the
implementation complexity. To address these
problems, many researches [6] have reported
to use the prior knowledge of the sampled signal to
enhance the performance of CS. Although most of
them focused on decoder-based reconstruction
optimization, encoder-based sampling optimization
may have important sense for addressing these
problems. Block-based sampling for fast CS of
RESEARCH ARTICLE OPEN ACCESS

natural images, where the original image is divided
into small blocks and each block is sampled
independently using the same measurement operator.
The possibility of exploiting block CS is motivated
by the great success of block DCT coding systems
which are widely used in the JPEG and the MPEG
standards[7]. The main advantages of our proposed
system include: (a) Measurement operator can be
easily stored and implemented through a random
under sampled filter bank. Block-based measurement
is more advantageous for real-time applications as the
encoder does not need to send the sampled data until
the whole image is measured; (b) Since each block is
processed independently, the initial solution can be
easily obtained and the reconstruction process can be
substantially speeded up; For natural images, our
preliminary results show that block CS systems offer
comparable performances to existing CS schemes
with much lower implementation cost.
III.IMAGE REPRESENTATION
The objective is to signify and express the
resulting aggregate of segmented pixels in a form
suitable for further computer processing after
segmenting an image into regions.
• Two choice for representing a region:
External characteristics: its boundary.
Internal characteristics: the pixels comprise the
region. For example, a region may be represented by
(a) its boundary with the boundary describe by
features such as its length, (b) the orientation of the
straight line joining the extreme points, and (c) the
number of concavities in the boundary.
• An external representation is chosen while the
primary focus is on shape characteristics.
• An internal representation is chosen when the
primary focus is on reflectivity properties, such as
color and texture. The segmentation techniques yield
raw data in the form of pixels along a boundary or
pixels contained in a region. Although these data are
sometimes used directly to obtain descriptors (as in
determining the texture of a region), standard practice
is to use schemes that compact the data into
representations that are considerably more useful in
the computation of descriptors. This section
introduces some basic representation schemes for this
purpose. To represent a boundary by a connected
sequence of straight line segments of specified length
and direction.
IV. IMAGE REPRESENTATION
TECHNIQUES
Over the last two decades, great
improvements have been made in image and video
compression techniques driven by a growing demand
for storage and transmission of visual information.
By far, many image and video compression
standards, such as JPEG, JPEG2000 and MPEG-4
AVC/H.264 etc., have been proposed. However,
these mainstream compression coding techniques still
may be not very efficiency in some special
compression applications, for examples of robust
image coding and encrypted image compression.
Robust coding is very important in digital multimedia
transmission over internet and wireless networks,
where the image codec needs to have not only
excellent compression performance to reduce data
rate, but also high robustness performance to resist
transmission error due to channel noise and/or packet
loss.
A) CRP Based Block Compressive Sensing
As mentioned above, the theory foundation
of CS is that the signal sampled is sparse or
compressible, and the required minimal number of
measurement dimension for perfect recovery is
determined by the sparsity of the sampled signal.
When an image is represented by a BCS scheme, it
will be inefficient to assign the same number of
measurement dimension to each sampled vector
corresponding to the different image block, because
the image block with different spatial characteristic
has significantly different sparsity from each other. In
general, the image blocks located at smooth region
should have stronger sparsity than those located at
rich edge or texture region. The method of bits
random permutations has been used in channel
coding in communication system for design of
interleaved, which can maximally scatter the burst
error generated in the process of data channel
transmission, thus plays a very important role in
increasing the reliability of data transmission.
Motivated by the above fact, the CRP in DCT
domain of image is exploited to equalize the sparsity
of the sampled vectors in CS encoding stage for
enhancing measurement efficiency of BCS of image
in this Section, which is followed by a CRP based
BCS scheme. The proposed CRP can be at the same
time used to achieve encrypting image in transform
domain at information owner side. In traditional
BCS, the CS sampling does not exploit the inter
coefficient correlation within a sampled signal vector,
so coefficient permutations across signal vectors will
not adversely affect encoding performance. On the
other hand, because the random Permutations make
the distribution of coefficients become randomization
and homogenization, such random permutations will
make sparsity of all the sampled vectors nearly
identical, which improves reconstruction
performance since it is likely that before CRP some
less sparse vectors will not be reconstructed well [8].

Figure 1: The architecture of BCS scheme with CRP
B) AS Based Block Compressive Sensing
In conventional CS scheme, a fixed
measurement matrix is used to achieve non-
adaptively sampling signal. This scheme is simple
but not high efficiency, because it identically samples
all signals with different feature, and non-
distinctively extracts all information components of a
signal that maybe have different importance to
recovery signal quality [9]. In this Section, a novel
method of developing adaptive measurement matrix
is proposed to achieve adaptive Sampling (AS) in our
BCS in DCT domain. As what we have known,
image signals are bandwidth limited, and their energy
are mostly distributed in the low-frequency
components. In addition, the human eyes have certain
masking effect on some parts of the frequency
spectrum of image signals, which like low-pass filters
that makes them more sensitive to low frequency
components than high frequency ones. So, the low
frequency components occupying large part of
energy of image signals are more important to image
quality than the high frequency components
occupying less part of energy. Considering these
facts, we propose to develop an adaptive
measurement matrix in BCS framework based on the
energy distribution characteristic of the sampled
image signal in DCT domain to enhance the
performance of BCS. The key idea is to adaptively
extract more information of the important frequency
components than that of the relatively non-important
frequency components in sampling stage to reduce
recovery error. It is implemented by adaptively
scaling the coefficients of measurement matrix
according to the sampled image‟s characteristic, i.e.
unequally measuring the different frequency
components based on their importance to image
quality.
C) Compressed Sensing
Compressed sensing is a signal processing
technique for ably acquire and reconstructing
a signal, by finding solution to underdetermined
linear systems. This takes profit of signal‟s
sparseness or compressibility in particular domain,
we design compressed data acquisition protocols
which perform as if it were possible to directly
acquire just allowing the entire signal to be
determined from relatively few measurements. The
important information about the signals/images in
effect, not acquiring that part of the data that would
eventually just be “thrown away” by lossy
compression[10]. Moreover, the protocols are non
adaptive and parallelizable; they do not require
knowledge of the signal/image to be acquired in
advance other than knowledge that the data will be
compressible and do not attempt any “understanding”
of the underlying object to guide an active or
adaptive sensing strategy. The measurements ended
in the compressed sensing protocol are holographic
thus, not easy pixel samples and must be process
nonlinearly. In specific applications, this principle
might enable dramatically reduced measurement
time, dramatically reduced sampling rates, or reduced
utilize of analog-to digital converter resources [10].
Shuffling the class attribute values
belonging to heterogeneous leaves of a decision tree.
If a leaf corresponds to a group of records having
different class attribute values, then the leaf is
identified to be a heterogeneous leaf.
Figure2: Overall joint source-channel coding system
block diagram [11]
Recently, we developed an interactive and
adaptive joint source-channel coding algorithm by
which the centre and size of the image are specified
by the user after receiving the low-resolution
background image.

D) Random Permutations Based Block
Bompressive Sensing
Compressive sensing (CS) is a novel theory
framework for signal acquisition, which has been
widely concerned in many application fields. A new
block CS scheme for image compression
applications, in which a method of coefficients
random permutations (CRP) in transform domain is
exploited for optimal sampling of CS, considering the
fact that the image blocks with different spatial
characteristics have different sparsity or
compressibility [12]. The proposed random
permutations technique makes the sparsity of all the
sampled coefficients vectors more evenly, which
results in requiring approximate equal number of
measurement for well restoration. New results show
that our proposed scheme can efficiently enhance the
CS performance in increasing reconstructed image
quality or reduce the measurement ratio.
(a) (b)
Figure 2: Visual quality comparisons with
measurement rate equaling to 0.4 and block size of
8×8. “(a) Original image, (b) reconstruction image by
CRP-based CS method.
A novel scheme of image compressive
sensing for compression applications is proposed in
this paper. By employing the coefficients random
permutations technique in sampling process, the
sparsity of all sampled vectors could be balanced
efficiently, which can considerably increase the
utilization of sensing resources. The simulation
results illustrate that our proposed sampling scheme
can enhance the performance of compressive sensing
efficiently in both objective and subjective
assessment manners with little increasing the
complexity.
E) Reweighted Compressive Sampling For Image
Compression
Shannon sampling theory tells us that if we
want to reconstruct a band limited signal without
distortion, then sampling the original signal at a rate
which is at least twice of its highest frequency is
necessary. However, a newly emerged theory called
Compressive Sampling (CS), also known as
Compressive Sensing proves that this is not always
true. It tells that as long as the signal is sparse or
compressible, then we can exactly recover the
original signals through only a few measurements,
namely, we can achieve data sampling and
compression at the same time for designing highly
efficient data compression techniques while the
performance of existing ones seem to reach the
bottleneck. Some initial exploration about applying
CS theory in data compression has been made;
however, the performance is limited. The main
problem that prevents CS framework from being
applied in real data compression system is the
efficiency decrease for complex signals which are
Usually hard to be perfectly sparsely represented.
One major reason causing this efficiency dropping is
that conventional CS framework samples data
randomly without showing discrimination to different
components, in other words, it does not consider
about the characteristics of the signals. However, this
may not be the best way to sample when it comes to
the complex signals, resembling images. A lot of
efforts have been made trying to improve the
performance of the CS signal recovery algorithms by
adjusting the behavior of the decoder adaptively to
the signal characteristics. However, usually their
methods are based on the blind estimation of the
signal magnitudes and iterative refining process in
the decoding side. For instance, in, the authors
propose an iterative reweighting scheme to enhance
the sparsity of the reconstruction results. In their
work, the solution of an unweighted minimization
reconstruction is used as the initial guess, and then
the weighting coefficients are refined iteratively, until
the convergence is reached. However, the complexity
for these iterative processing schemes is too high for
the methods to be practical, and they are often
guaranteed only to converge to a local (not
necessarily) optimum. In this paper, we propose a
reweighted Compressive Sampling for CS based
image compression by introducing a weighting
scheme into the encoding procedure. This method
works on the block-based processing mechanism.
First the images will be divided into small blocks,
and then the weighting coefficients are determined by
taking advantage of the statistical characteristics of
natural images and subsequently sent together with
the CS measurements to help the decoder to achieve
better reconstruction results. The main advantage of

our proposed method is that it can sufficiently utilize
image characteristics to achieve considerable
performance gain without notably increasing the
complexity of the system structure or the
computation procedures [13].
G) Robust Image Compression Based On
Compressing Sensing
The existing image compression methods
(e.g., JPEG2000, etc.) are vulnerable to bit-loss, and
this is generally tackle by channel coding that follow.
Though, source coding and channel coding have
contradictory requirement. In this paper, we deal with
the problem with an alternative paradigm, and a
novel compressive sensing (CS) based compression
scheme is therefore proposed [14]. Discrete wavelet
transform (DWT) is applied for sparse representation,
and base on the property of 2-D DWT, a fast CS
measurements captivating technique is presented.
Unlike the unequally important discrete wavelet
coefficients, the resultant CS measurements hold
nearly the same amount of information and have
minimal effects for bit-loss. At the decoder side, one
can merely recreate the image via minimization.
New-results show that the proposed CS-based image
codec without resorting to error protection is more
robust compared with existing CS technique and
relevant joint source channel coding (JSCC) schemes.
V. CONCLUSION
With the help of compressive sensing using
the recently developed HALS algorithm based on
nonlinear thresholding a progressive transmission
system has been introduced in this paper. The main
contributions of this effort are on a. selection and
coding of a small number of samples (sampled below
Nyquist rate) and b. introducing an adaptive
thresholding technique for selection and
reconstruction of those samples. Keep the inferences
that disclose private information about organizations
or individuals at a minimum.Compression of
encrypted image is not possible by using any of the
classical compression techniques. We proposed a
system and showed that lossy compression of
encrypted image data is indeed possible by using
compressive sensing techniques. The basis pursuit
algorithm was appropriately modified to enable joint
decompression and decryption. Simulation results
were provided to demonstrate the compression results
of the proposed method.
REFERENCES
[1] S. Mun, J.E. Fowler, Block compressed
sensing of images using directional
Transforms, in: Proc. Int. Conf. Image
Processing (ICIP), 2009, pp. 3021–3024.
[2] M. Johnson, P. Ishwar, V. Prabhakaran, D.
Schonberg and K. Ramachandran, ”On
compressing encrypted data”, IEEE Trans.
Signal Processing vol. 52, pp. 2992-3006, Oct.
2004.
[3] D. Schonberg, S. Draper and K.
Ramachandran, ”On compression of encrypted
images”, Proc. International conference on
Image processing, Atlanta, GA, pp. 269-272,
Oct. 2006.
[4] A. A. Kumar and A. Makur, ”Distributed
source coding based encryption and lossless
compression of gray scale and color images”,
Proc. IEEE 10th workshop on multimedia and
signal processing, Cairns, Australia, pp. 760-
764, Oct. 2008..
[5] A. Mitra, Y. V. S. Rao and S. R. M. Prasanna,
” A new image encryption approach using
combinational permutation techniques”,
International J. of comp. Sc., Vol. 1, PP. 127-
131, May 2006.
[6] L. Gan, Block compressed sensing of natural
images, in: Proc. of Int. Conf. Digital Signal
Processing, 2007, pp. 403–406.
[7] R. Baranuik, ”Compressive Sensing”, IEEE
signal processing magazine, Vol. 24, PP. 118-
120, July 2007.
[8] L. Gan, Block compressed sensing of natural
images, in: Proc. of Int. Conf. Digital Signal
Processing, 2007, pp. 403–406.
[9] E. J. Candes and M. B. Wakin, ”An
introduction to compressive sampling”, IEEE
signal processing magazine, Vol. 25, pp. 21-
30, Mar. 2008.
[10] D.L. Donoho, Compressed sensing, IEEE
Trans. Inform. Theory 52 (4) (2006) 1289–
1306.
[11] S. Sanei, A. H. Phan, J.-L. Lo V. Abolghasemi
and A. Cichocki, „„A compressive sensing
approach for progressive transmission of
images‟‟, in Proc. Int. Conf. DSP‟2009, pp. 1–
5, 2009.
[12] Z. Gao, C. Xiong, C. Zhou, H. Wang,
“Compressive Sampling with Coefficients
Random Permutations for Image
Compression”, in: Proc. Int. Conf. on
Multimedia and Signal Processing, 2011, pp.
321–324.
[13] Y. Yang, O.C. Au, L. Fang, X. Wen, W. Tang,
Reweighted compressive sampling for image
compression, in: Proc. Int. Conf. Picture
Coding Symposium (PCS), 2009, pp. 89–92.
[14] C. Deng, W. Lin, B.-S. Lee, C.T. Lau, Robust
image compression based on compressive
sensing, in: Proc. Int. Conf. Multimedia &
Expo (ICME), 2010, pp. 462–467
[14] R.G. Baraniuk, V. Cevher, M. Duarte, C.
Hegde, Model-based compressive sensing,
IEEE Trans. Inf. Theory 56 (4) (2010) 1982–
2001.

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