Abstract: To compress, improve bit error performance and also enhance 2D images, a new scheme, called Iterative Cellular-Turbo System (IC-TS) is introduced. In IC-TS, the original image is partitioned into 2N quantization levels, where N is denoted as bit planes. Then each of the N-bit-plane is coded by Turbo encoder and transmitted over Additive White Gaussian Noise (AWGN) channel. At the receiver side, bit-planes are re-assembled taking into consideration of neighborhood relationship of pixels in 2-D images. Each of the noisy bit-plane values of the image is evaluated iteratively using IC-TS structure, which is composed of equalization block; Iterative Cellular Image Processing Algorithm (ICIPA) and Turbo decoder. In IC-TS, there is an iterative feedback link between ICIPA and Turbo decoder. ICIPA uses mean and standard deviation of estimated values of each pixel neighborhood. It has extra-ordinary satisfactory results of both Bit Error Rate (BER) and image enhancement performance for less than -1 dB Signal-to-Noise Ratio (SNR) values, compared to traditional turbo coding scheme and 2-D filtering, applied separately. Also, compression can be achieved by using IC-TS systems. In compression, less memory storage is used and data rate is increased up to N-1 times by simply choosing any number of bit slices, sacrificing resolution. Hence, it is concluded that IC-TS system will be a compromising approach in 2-D image transmission, recovery of noisy signals and image compression.
Abstract: Image coding based on clustering provides immediate
access to targeted features of interest in a high quality decoded
image. This approach is useful for intelligent devices, as well as for
multimedia content-based description standards. The result of image
clustering cannot be precise in some positions especially on pixels
with edge information which produce ambiguity among the clusters.
Even with a good enhancement operator based on PDE, the quality of
the decoded image will highly depend on the clustering process. In
this paper, we introduce an ambiguity cluster in image coding to
represent pixels with vagueness properties. The presence of such
cluster allows preserving some details inherent to edges as well for
uncertain pixels. It will also be very useful during the decoding phase
in which an anisotropic diffusion operator, such as Perona-Malik,
enhances the quality of the restored image. This work also offers a
comparative study to demonstrate the effectiveness of a fuzzy
clustering technique in detecting the ambiguity cluster without losing
lot of the essential image information. Several experiments have been
carried out to demonstrate the usefulness of ambiguity concept in
image compression. The coding results and the performance of the
proposed algorithms are discussed in terms of the peak signal-tonoise
ratio and the quantity of ambiguous pixels.
Abstract: In this paper we present a novel approach for wavelet compression of electrocardiogram (ECG) signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. SPIHT algorithm has achieved prominent success in image compression. Here we use a modified version of SPIHT for one dimensional signals. We applied wavelet transform with SPIHT coding algorithm on different records of MIT-BIH database. The results show the high efficiency of this method in ECG compression.
Abstract: This paper concerns about the experimental and
numerical investigations of energy absorption and axial tearing
behaviour of aluminium 6060 circular thin walled tubes under static
axial compression. The tubes are received in T66 heat treatment
condition with fixed outer diameter of 42mm, thickness of 1.5mm
and length of 120mm. The primary variables are the conical die
angles (15°, 20° and 25°). Numerical simulations are carried on
ANSYS/LS-DYNA software tool, for investigating the effect of
friction between the tube and the die.
Abstract: While compressing text files is useful, compressing
still image files is almost a necessity. A typical image takes up much
more storage than a typical text message and without compression
images would be extremely clumsy to store and distribute. The
amount of information required to store pictures on modern
computers is quite large in relation to the amount of bandwidth
commonly available to transmit them over the Internet and
applications. Image compression addresses the problem of reducing
the amount of data required to represent a digital image. Performance
of any image compression method can be evaluated by measuring the
root-mean-square-error & peak signal to noise ratio. The method of
image compression that will be analyzed in this paper is based on the
lossy JPEG image compression technique, the most popular
compression technique for color images. JPEG compression is able to
greatly reduce file size with minimal image degradation by throwing
away the least “important" information. In JPEG, both color
components are downsampled simultaneously, but in this paper we
will compare the results when the compression is done by
downsampling the single chroma part. In this paper we will
demonstrate more compression ratio is achieved when the
chrominance blue is downsampled as compared to downsampling the
chrominance red in JPEG compression. But the peak signal to noise
ratio is more when the chrominance red is downsampled as compared
to downsampling the chrominance blue in JPEG compression. In
particular we will use the hats.jpg as a demonstration of JPEG
compression using low pass filter and demonstrate that the image is
compressed with barely any visual differences with both methods.
Abstract: Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, the given image may
contain a number of distinct gray levels with narrow difference with
their neighborhood pixels. If the gray levels of the pixels in an image
and their neighbors are mapped in such a way that the difference in
the gray levels of the neighbors with the pixel is minimum, then
compression ratio as well as the convergence of the network can be
improved. To achieve this, a Cumulative distribution function is
estimated for the image and it is used to map the image pixels. When
the mapped image pixels are used, the Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
Abstract: In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. 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 time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.
Abstract: Wavelet transforms is a very powerful tools for image compression. One of its advantage is the provision of both spatial and frequency localization of image energy. However, wavelet transform coefficients are defined by both a magnitude and sign. While algorithms exist for efficiently coding the magnitude of the transform coefficients, they are not efficient for the coding of their sign. It is generally assumed that there is no compression gain to be obtained from the coding of the sign. Only recently have some authors begun to investigate the sign of wavelet coefficients in image coding. Some authors have assumed that the sign information bit of wavelet coefficients may be encoded with the estimated probability of 0.5; the same assumption concerns the refinement information bit. In this paper, we propose a new method for Separate Sign Coding (SSC) of wavelet image coefficients. The sign and the magnitude of wavelet image coefficients are examined to obtain their online probabilities. We use the scalar quantization in which the information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also examined. We show that the sign information and the refinement information may be encoded by the probability of approximately 0.5 only after about five bit planes. Two maps are separately entropy encoded: the sign map and the magnitude map. The refinement information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also entropy encoded. An algorithm is developed and simulations are performed on three standard images in grey scale: Lena, Barbara and Cameraman. Five scales are performed using the biorthogonal wavelet transform 9/7 filter bank. The obtained results are compared to JPEG2000 standard in terms of peak signal to noise ration (PSNR) for the three images and in terms of subjective quality (visual quality). It is shown that the proposed method outperforms the JPEG2000. The proposed method is also compared to other codec in the literature. It is shown that the proposed method is very successful and shows its performance in term of PSNR.
Abstract: Large volumes of fingerprints are collected and stored
every day in a wide range of applications, including forensics, access
control etc. It is evident from the database of Federal Bureau of
Investigation (FBI) which contains more than 70 million finger
prints. Compression of this database is very important because of this
high Volume. The performance of existing image coding standards
generally degrades at low bit-rates because of the underlying block
based Discrete Cosine Transform (DCT) scheme. Over the past
decade, the success of wavelets in solving many different problems
has contributed to its unprecedented popularity. Due to
implementation constraints scalar wavelets do not posses all the
properties which are needed for better performance in compression.
New class of wavelets called 'Multiwavelets' which posses more
than one scaling filters overcomes this problem. The objective of this
paper is to develop an efficient compression scheme and to obtain
better quality and higher compression ratio through multiwavelet
transform and embedded coding of multiwavelet coefficients through
Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm.
A comparison of the best known multiwavelets is made to the best
known scalar wavelets. Both quantitative and qualitative measures of
performance are examined for Fingerprints.
Abstract: In this study, to compress ECG signals, KLT (Karhunen-
Loeve Transform) method has been used. The purpose of this method is to
perform effective ECG coding by a correlation between the length of frames
and the number of vectors of ECG signals.
Abstract: This paper deals with analysis of flexural stiffness,
indentation and their energies in three point loading of sandwich
beams with composite faces from Eglass/epoxy and cores from
Polyurethane or PVC. Energy is consumed in three stages of
indentation in laminated beam, indentation of sandwich beam and
bending of sandwich beam. Theory of elasticity is chosen to present
equations for indentation of laminated beam, then these equations
have been corrected to offer better results. An analytical model has
been used assuming an elastic-perfectly plastic compressive behavior
of the foam core. Classical theory of beam is used to describe three
point bending. Finite element (FE) analysis of static indentation
sandwich beams is performed using the FE code ABAQUS. The
foam core is modeled using the crushable foam material model and
response of the foam core is experimentally characterized in uniaxial
compression.
Three point bending and indentation have been done
experimentally in two cases of low velocity and higher velocity
(quasi-impact) of loading. Results can describe response of beam in
terms of core and faces thicknesses, core material, indentor diameter,
energy absorbed, and length of plastic area in the testing. The
experimental results are in good agreement with the analytical and
FE analyses. These results can be used as an introduction for impact
loading and energy absorbing of sandwich structures.
Abstract: In real-field applications, the correct determination of voice segments highly improves the overall system accuracy and minimises the total computation time. This paper presents reliable measures of speech compression by detcting the end points of the speech signals prior to compressing them. The two different compession schemes used are the Global threshold and the Level- Dependent threshold techniques. The performance of the proposed method is tested wirh the Signal to Noise Ratios, Peak Signal to Noise Ratios and Normalized Root Mean Square Error parameter measures.
Abstract: The scientific community has invested a great deal of effort in the fields of discrete wavelet transform in the last few decades. Discrete wavelet transform (DWT) associated with the vector quantization has been proved to be a very useful tool for the compression of image. However, the DWT is very computationally intensive process requiring innovative and computationally efficient method to obtain the image compression. The concurrent transformation of the image can be an important solution to this problem. This paper proposes a model of concurrent DWT for image compression. Additionally, the formal verification of the model has also been performed. Here the Symbolic Model Verifier (SMV) has been used as the formal verification tool. The system has been modeled in SMV and some properties have been verified formally.
Abstract: In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.
Abstract: Medical imaging uses the advantage of digital
technology in imaging and teleradiology. In teleradiology systems
large amount of data is acquired, stored and transmitted. A major
technology that may help to solve the problems associated with the
massive data storage and data transfer capacity is data compression
and decompression. There are many methods of image compression
available. They are classified as lossless and lossy compression
methods. In lossy compression method the decompressed image
contains some distortion. Fractal image compression (FIC) is a lossy
compression method. In fractal image compression an image is
coded as a set of contractive transformations in a complete metric
space. The set of contractive transformations is guaranteed to
produce an approximation to the original image. In this paper FIC is
achieved by PIFS using quadtree partitioning. PIFS is applied on
different images like , Ultrasound, CT Scan, Angiogram, X-ray,
Mammograms. In each modality approximately twenty images are
considered and the average values of compression ratio and PSNR
values are arrived. In this method of fractal encoding, the
parameter, tolerance factor Tmax, is varied from 1 to 10, keeping the
other standard parameters constant. For all modalities of images the
compression ratio and Peak Signal to Noise Ratio (PSNR) are
computed and studied. The quality of the decompressed image is
arrived by PSNR values. From the results it is observed that the
compression ratio increases with the tolerance factor and
mammogram has the highest compression ratio. The quality of the
image is not degraded upto an optimum value of tolerance factor,
Tmax, equal to 8, because of the properties of fractal compression.
Abstract: This paper presents image compression with wavelet based method. The wavelet transformation divides image to low- and high pass filtered parts. The traditional JPEG compression technique requires lower computation power with feasible losses, when only compression is needed. However, there is obvious need for wavelet based methods in certain circumstances. The methods are intended to the applications in which the image analyzing is done parallel with compression. Furthermore, high frequency bands can be used to detect changes or edges. Wavelets enable hierarchical analysis for low pass filtered sub-images. The first analysis can be done for a small image, and only if any interesting is found, the whole image is processed or reconstructed.
Abstract: In this work, we present a comparison between two
techniques of image compression. In the first case, the image is
divided in blocks which are collected according to zig-zag scan. In
the second one, we apply the Fast Cosine Transform to the image,
and then the transformed image is divided in blocks which are
collected according to zig-zag scan too. Later, in both cases, the
Karhunen-Loève transform is applied to mentioned blocks. On the
other hand, we present three new metrics based on eigenvalues for a
better comparative evaluation of the techniques. Simulations show
that the combined version is the best, with minor Mean Absolute
Error (MAE) and Mean Squared Error (MSE), higher Peak Signal to
Noise Ratio (PSNR) and better image quality. Finally, new technique
was far superior to JPEG and JPEG2000.
Abstract: A theory for optimal filtering of infinite sets of random
signals is presented. There are several new distinctive features of the
proposed approach. First, a single optimal filter for processing any
signal from a given infinite signal set is provided. Second, the filter is
presented in the special form of a sum with p terms where each term
is represented as a combination of three operations. Each operation
is a special stage of the filtering aimed at facilitating the associated
numerical work. Third, an iterative scheme is implemented into the
filter structure to provide an improvement in the filter performance at
each step of the scheme. The final step of the scheme concerns signal
compression and decompression. This step is based on the solution of
a new rank-constrained matrix approximation problem. The solution
to the matrix problem is described in this paper. A rigorous error
analysis is given for the new filter.
Abstract: In general the images used for compression are of
different types like dark image, high intensity image etc. When these
images are compressed using Counter Propagation Neural Network,
it takes longer time to converge. The reason for this is that the given
image may contain a number of distinct gray levels with narrow
difference with their neighborhood pixels. If the gray levels of the
pixels in an image and their neighbors are mapped in such a way that
the difference in the gray levels of the neighbor with the pixel is
minimum, then compression ratio as well as the convergence of the
network can be improved. To achieve this, a Cumulative Distribution
Function is estimated for the image and it is used to map the image
pixels. When the mapped image pixels are used the Counter
Propagation Neural Network yield high compression ratio as well as
it converges quickly.
Abstract: Vapour recompression system has been used to
enhance reduction in energy consumption and improvement in
energy effectiveness of distillation columns. However, the effects of
certain parameters have not been taken into consideration. One of
such parameters is the column heat loss which has either been
assumed to be a certain percent of reboiler heat transfer or negligible.
The purpose of this study was to evaluate the heat loss from an
ethanol-water vapour recompression distillation column with
pressure increase across the compressor (VRCAS) and compare the
results obtained and its effect on some parameters in similar system
(VRCCS) where the column heat loss has been assumed or neglected.
Results show that the heat loss evaluated was higher when compared
with that obtained for the column VRCCS. The results also showed
that increase in heat loss could have significant effect on the total
energy consumption, reboiler heat transfer, the number of trays and
energy effectiveness of the column.