This document proposes a data compression technique using double transforms. It compares using the Walsh-Hadamard Transform (WHT), Discrete Cosine Transform (DCT), and a proposed Walsh-Cosine double transform on several contour data sets. The double transform approach applies the WHT, selects some spectral components, converts the spectrum to the DCT domain, and applies the inverse DCT. Experimental results show the double transform reduces multiplication operations compared to DCT alone while maintaining reconstruction error.
Finite-difference modeling, accuracy, and boundary conditions- Arthur Weglein...Arthur Weglein
This short report gives a brief review on the finite difference modeling method used in MOSRP
and its boundary conditions as a preparation for the Green’s theorem RTM. The first
part gives the finite difference formulae we used and the second part describes the implemented
boundary conditions. The last part, using two examples, points out some impacts of the accuracy
of source fields on the results of modeling.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
A Novel Cosine Approximation for High-Speed Evaluation of DCTCSCJournals
This article presents a novel cosine approximation for high-speed evaluation of DCT (Discrete Cosine Transform) using Ramanujan Ordered Numbers. The proposed method uses the Ramanujan ordered number to convert the angles of the cosine function to integers. Evaluation of these angles is by using a 4th degree Polynomial that approximates the cosine function with error of approximation in the order of 10^-3. The evaluation of the cosine function is explained through the computation of the DCT coefficients. High-speed evaluation at the algorithmic level is measured in terms of the computational complexity of the algorithm. The proposed algorithm of cosine approximation increases the overhead on the number of adders by 13.6%. This algorithm avoids floating-point multipliers and requires N/2log2N shifts and (3N/2 log2 N)- N + 1 addition operations to evaluate an N-point DCT coefficients thereby improving the speed of computation of the coefficients .
Finite-difference modeling, accuracy, and boundary conditions- Arthur Weglein...Arthur Weglein
This short report gives a brief review on the finite difference modeling method used in MOSRP
and its boundary conditions as a preparation for the Green’s theorem RTM. The first
part gives the finite difference formulae we used and the second part describes the implemented
boundary conditions. The last part, using two examples, points out some impacts of the accuracy
of source fields on the results of modeling.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
A Novel Cosine Approximation for High-Speed Evaluation of DCTCSCJournals
This article presents a novel cosine approximation for high-speed evaluation of DCT (Discrete Cosine Transform) using Ramanujan Ordered Numbers. The proposed method uses the Ramanujan ordered number to convert the angles of the cosine function to integers. Evaluation of these angles is by using a 4th degree Polynomial that approximates the cosine function with error of approximation in the order of 10^-3. The evaluation of the cosine function is explained through the computation of the DCT coefficients. High-speed evaluation at the algorithmic level is measured in terms of the computational complexity of the algorithm. The proposed algorithm of cosine approximation increases the overhead on the number of adders by 13.6%. This algorithm avoids floating-point multipliers and requires N/2log2N shifts and (3N/2 log2 N)- N + 1 addition operations to evaluate an N-point DCT coefficients thereby improving the speed of computation of the coefficients .
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Initial Phase Effect on DOA Estimation in MMIMO Using Separated Steering MatrixTELKOMNIKA JOURNAL
Providing simple and low complexity algorithms for estimating the direction of arrival in large systems using Massive MIMO is considered an important issue. In this paper a method with reduced complexity was proposed to estimate the direction of arrival in FD- MMIMO. The Separated Steering Matrix (SSM) algorithm uses two separated equations for estimating elevation and azimuth angles of Multi-users. This method reduces the complexity of calculating the covariance matrix by decreasing the size of this matrix. This technique is tested using 2D-MUSIC algorithm. Since the mobility of devices affects the accuracy of direction estimation, thus the effect of the initial phase of transmitted signal from mobile device is tested.
I am Mathew K. I am a Planetary Science Assignment Expert at eduassignmenthelp.com. I hold a Masters’s Degree in Planetary Science, The University of Chicago, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Planetary Sciences.
Visit eduassignmenthelp.com or email info@eduassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Planetary Science Assignments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Investigation on the Pattern Synthesis of Subarray Weights for Low EMI Applic...IOSRJECE
In modern radar applications, it is frequently required to produce sum and difference patterns sequentially. The sum pattern amplitude coefficients are obtained by using Dolph-Chebyshev synthesis method where as the difference pattern excitation coefficients will be optimized in this present work. For this purpose optimal group weights will be introduced to the different array elements to obtain any type of beam depending on the application. Optimization of excitation to the array elements is the main objective so in this process a subarray configuration is adopted. However, Differential Evolution Algorithm is applied for optimization method. The proposed method is reliable and accurate. It is superior to other methods in terms of convergence speed and robustness. Numerical and simulation results are presented.
A Novel Algorithm for Watermarking and Image Encryption cscpconf
Digital watermarking is a method of copyright protection of audio, images, video and text. We
propose a new robust watermarking technique based on contourlet transform and singular value
decomposition. The paper also proposes a novel encryption algorithm to store a signed double
matrix as an RGB image. The entropy of the watermarked image and correlation coefficient of
extracted watermark image is very close to ideal values, proving the correctness of proposed
algorithm. Also experimental results show resiliency of the scheme against large blurring attack
like mean and gaussian filtering, linear filtering (high pass and low pass filtering) , non-linear
filtering (median filtering), addition of a constant offset to the pixel values and local exchange of pixels .Thus proving the security, effectiveness and robustness of the proposed watermarking algorithm.
I am Irene M. I am a Diffusion Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from California, USA.
I have been helping students with their homework for the past 8 years. I solve assignments related to Diffusion. Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Diffusion Assignments.
Performance Analysis of Image Enhancement Using Dual-Tree Complex Wavelet Tra...IJERD Editor
Resolution enhancement (RE) schemes which are not based on wavelets has one of the major
drawbacks of losing high frequency contents which results in blurring. The discrete wavelet- transform-based
(DWT) Resolution Enhancement scheme generates artifacts (due to a DWT shift-variant property). A wavelet-
Domain approach based on dual-tree complex wavelet transform (DT-CWT) & nonlocal means (NLM) is
proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly
shift invariant) to obtain high-frequency sub bands. Here the Lanczos interpolator is used to interpolate the highfrequency
sub bands & the low-resolution (LR) input image. The high frequency sub bands are passed through
an NLM filter to cater for the artifacts generated by DT-CWT (despite of it’s nearly shift invariance). The
filtered high-frequency sub bands and the LR input image are combined by using inverse DTCWT to obtain a
resolution-enhanced image. Objective and subjective analyses show superiority of the new proposed technique
over the conventional and state-of-the-art RE techniques.
PERFORMANCE EVALUATION OF DIFFERENT TECHNIQUES FOR TEXTURE CLASSIFICATION cscpconf
Texture is the term used to characterize the surface of a given object or phenomenon and is an
important feature used in image processing and pattern recognition. Our aim is to compare
various Texture analyzing methods and compare the results based on time complexity and
accuracy of classification. The project describes texture classification using Wavelet Transform
and Co occurrence Matrix. Comparison of features of a sample texture with database of
different textures is performed. In wavelet transform we use the Haar, Symlets and Daubechies
wavelets. We find that, thee ‘Haar’ wavelet proves to be the most efficient method in terms of
performance assessment parameters mentioned above. Comparison of Haar wavelet and Cooccurrence
matrix method of classification also goes in the favor of Haar. Though the time
requirement is high in the later method, it gives excellent results for classification accuracy
except if the image is rotated.
DISTINGUISH BETWEEN WALSH TRANSFORM AND HAAR TRANSFORMDip transformsNITHIN KALLE PALLY
walsh transform-1D Walsh Transform kernel is given by:
n - 1
g(x, u) = (1/N) ∏ (-1) bi(x) bn-1-i(u)
i = 0
where, N – no. of samples
n – no. of bits needed to represent x as well as u
bk(z) – kth bits in binary representation of z.
Thus, Forward Discrete Walsh Transformation is
N - 1 n - 1
W(u) = (1/N) Σ f(x) ∏ (-1) bi(x) b(u) x = 0 i = 0
A presentation on how moving quickly through an identity, credential and access control life-cycle matters, and don't be afraid to to say we failed (hint of government - don't wait for the IG...)
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Initial Phase Effect on DOA Estimation in MMIMO Using Separated Steering MatrixTELKOMNIKA JOURNAL
Providing simple and low complexity algorithms for estimating the direction of arrival in large systems using Massive MIMO is considered an important issue. In this paper a method with reduced complexity was proposed to estimate the direction of arrival in FD- MMIMO. The Separated Steering Matrix (SSM) algorithm uses two separated equations for estimating elevation and azimuth angles of Multi-users. This method reduces the complexity of calculating the covariance matrix by decreasing the size of this matrix. This technique is tested using 2D-MUSIC algorithm. Since the mobility of devices affects the accuracy of direction estimation, thus the effect of the initial phase of transmitted signal from mobile device is tested.
I am Mathew K. I am a Planetary Science Assignment Expert at eduassignmenthelp.com. I hold a Masters’s Degree in Planetary Science, The University of Chicago, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Planetary Sciences.
Visit eduassignmenthelp.com or email info@eduassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Planetary Science Assignments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Investigation on the Pattern Synthesis of Subarray Weights for Low EMI Applic...IOSRJECE
In modern radar applications, it is frequently required to produce sum and difference patterns sequentially. The sum pattern amplitude coefficients are obtained by using Dolph-Chebyshev synthesis method where as the difference pattern excitation coefficients will be optimized in this present work. For this purpose optimal group weights will be introduced to the different array elements to obtain any type of beam depending on the application. Optimization of excitation to the array elements is the main objective so in this process a subarray configuration is adopted. However, Differential Evolution Algorithm is applied for optimization method. The proposed method is reliable and accurate. It is superior to other methods in terms of convergence speed and robustness. Numerical and simulation results are presented.
A Novel Algorithm for Watermarking and Image Encryption cscpconf
Digital watermarking is a method of copyright protection of audio, images, video and text. We
propose a new robust watermarking technique based on contourlet transform and singular value
decomposition. The paper also proposes a novel encryption algorithm to store a signed double
matrix as an RGB image. The entropy of the watermarked image and correlation coefficient of
extracted watermark image is very close to ideal values, proving the correctness of proposed
algorithm. Also experimental results show resiliency of the scheme against large blurring attack
like mean and gaussian filtering, linear filtering (high pass and low pass filtering) , non-linear
filtering (median filtering), addition of a constant offset to the pixel values and local exchange of pixels .Thus proving the security, effectiveness and robustness of the proposed watermarking algorithm.
I am Irene M. I am a Diffusion Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from California, USA.
I have been helping students with their homework for the past 8 years. I solve assignments related to Diffusion. Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Diffusion Assignments.
Performance Analysis of Image Enhancement Using Dual-Tree Complex Wavelet Tra...IJERD Editor
Resolution enhancement (RE) schemes which are not based on wavelets has one of the major
drawbacks of losing high frequency contents which results in blurring. The discrete wavelet- transform-based
(DWT) Resolution Enhancement scheme generates artifacts (due to a DWT shift-variant property). A wavelet-
Domain approach based on dual-tree complex wavelet transform (DT-CWT) & nonlocal means (NLM) is
proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly
shift invariant) to obtain high-frequency sub bands. Here the Lanczos interpolator is used to interpolate the highfrequency
sub bands & the low-resolution (LR) input image. The high frequency sub bands are passed through
an NLM filter to cater for the artifacts generated by DT-CWT (despite of it’s nearly shift invariance). The
filtered high-frequency sub bands and the LR input image are combined by using inverse DTCWT to obtain a
resolution-enhanced image. Objective and subjective analyses show superiority of the new proposed technique
over the conventional and state-of-the-art RE techniques.
PERFORMANCE EVALUATION OF DIFFERENT TECHNIQUES FOR TEXTURE CLASSIFICATION cscpconf
Texture is the term used to characterize the surface of a given object or phenomenon and is an
important feature used in image processing and pattern recognition. Our aim is to compare
various Texture analyzing methods and compare the results based on time complexity and
accuracy of classification. The project describes texture classification using Wavelet Transform
and Co occurrence Matrix. Comparison of features of a sample texture with database of
different textures is performed. In wavelet transform we use the Haar, Symlets and Daubechies
wavelets. We find that, thee ‘Haar’ wavelet proves to be the most efficient method in terms of
performance assessment parameters mentioned above. Comparison of Haar wavelet and Cooccurrence
matrix method of classification also goes in the favor of Haar. Though the time
requirement is high in the later method, it gives excellent results for classification accuracy
except if the image is rotated.
DISTINGUISH BETWEEN WALSH TRANSFORM AND HAAR TRANSFORMDip transformsNITHIN KALLE PALLY
walsh transform-1D Walsh Transform kernel is given by:
n - 1
g(x, u) = (1/N) ∏ (-1) bi(x) bn-1-i(u)
i = 0
where, N – no. of samples
n – no. of bits needed to represent x as well as u
bk(z) – kth bits in binary representation of z.
Thus, Forward Discrete Walsh Transformation is
N - 1 n - 1
W(u) = (1/N) Σ f(x) ∏ (-1) bi(x) b(u) x = 0 i = 0
A presentation on how moving quickly through an identity, credential and access control life-cycle matters, and don't be afraid to to say we failed (hint of government - don't wait for the IG...)
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.
Digital Signal Processing[ECEG-3171]-Ch1_L06Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Singular Value Decomposition: Principles and Applications in Multiple Input M...IJCNCJournal
The authors discuss the importance of using the singular value decomposition (SVD) in computing the capacity of multiple input multiple output (MIMO) and in estimation the channel gain from the transmitter to the receiver. Examples that show how the SVD simplifies computing the MIMO channel capacity are discussed. Numerical results that show what factors determine the performance of using SVD in channel
estimation are also discussed.
this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered.this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered.this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered.this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered.this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered.this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered. this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns for first mid examinations in this 2.5 units covered.this is the mcqs for jntuh 3rd year students digital signal processing r18 studetns fo
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
Method for Converter Synchronization with RF InjectionCSCJournals
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Multi carrier equalization by restoration of redundanc y (merry) for adaptive...IJNSA Journal
This paper proposes a new blind adaptive channel shortening approach for multi-carrier systems. The
performance of the discrete Fourier transform-DMT (DFT-DMT) system is investigated with the proposed
DST-DMT system over the standard carrier serving area (CSA) loop1. Enhanced bit rates demonstrated
and less complexity also involved by the simulation of the DST-DMT system.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
Adaptive Channel Equalization using Multilayer Perceptron Neural Networks wit...IOSRJVSP
This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN). The channel equalizer is modelled as a non-linear Multilayer Perceptron (MLP) structure. The Back Propagation (BP) algorithm is used to optimize the synaptic weights of the equalizer during the training mode. In the typical BP algorithm, the error signal is propagated from the output layer to the input layer while the learning rate parameter is held constant. In this study, the BP algorithm is modified so as to allow for the learning rate to be variable at each iteration and this achieves a faster convergence. The proposed algorithm is used to train the MLP based decision feedback equalizer (DFE) for time dispersive ISI channels. The equalizer is tested for a random input sequence of BPSK signals and its performance analysed in terms of the Bit Error Rates and speed of convergence. Simulation results show that the proposed algorithm improves the Bit Error Rate (BER) and rate of convergence.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Double transform contoor extraction
1. Double Transform
Contour Compression
W. Besbas, M. Arteimi, F. Belgassem
7th April University, Faculty of Electronic Engineering,
Faculty of science,P.O.Box 38645 Beni Walid, Libya
P.O.Box 116418 Zawia, LibyaE-mail : bfituri@hotmail.com
E-mail : arteimi@yahoo.com
Key words: Data Compression, Transform coding, Double Transforms, Mixed Transforms,
Contour Compression, Contour Data Reduction, Zonal sample reduction, Threshold sample
reduction.
ABSTRACT
In this paper a data compression technique using double transforms is proposed. The
compression ability of any transform or a combination of transforms is evaluated by the error in
the reconstructed data and/or its fast implementation. This investigation concerns the reduction
in the computational requirements for data compression without increasing, significantly, the
error in reconstruction. The double transform approach is based on the transformation of input
data using WHT, rejecting some of the spectral components, converting the spectrum from
WHT domain into DCT domain, and then inverse transform using DCT. In the experimental
part of this paper, several contours extracted from 2D images, using the FCCE method for
contour extraction, ware used. It is shown that the proposed technique reduces the number of
multiplication operations, comparing with DCT. In addition, the compression process does not
require any multiplications at the transmitting end, thus can be used for real time compression
applications.
1. INTRODUCTION
In this investigation a data compression technique using double transforms is proposed.
The analysis of the contour data compression using the zonal sample reduction scheme is
presented. The performances of the Walsh-Hadamard Transform (WHT), Discrete Cosine
Transform (DCT), and the proposed Walsh-Cosine double transform, are compared using the
data of several contours.
WHT has good performance with respect to its fast implementation. The WHT does
not require any multiplication operations for transforming input data from spatial domain into
the spectral domain[1]. DCT is known for its good performance in data compression
applications, comparing with other transforms such as Discrete Sine Transform (DST), Haar,
and WHT transforms[2,3].This has led to the development of many algorithms for the fast
computation of DCT transform[4].
Transform data compression can be achieved using either threshold sampling or zonal
sampling schemes[5]. In threshold sampling, the transform domain samples which are above a
certain level are selected. The zonal sampling selects the transform domain samples that are
located in a predetermined zone, and the rest are set to zero. Data compression using threshold
sampling scheme requires the position of the selected samples as an over-head information.
2. The compression ability of any transform or a combination of transforms is evaluated
with respect to the error in the reconstructed data and/or the computational requirements.
Ramaswamy and Mikheal, in [6], have developed an image compression technique using mixed
transforms. In this technique the image is represented employing subsets of basis functions of
two transforms. The image is reconstructed at the encoder and the residual error is also
transmitted using a lossless technique. This received error is then added to the reconstructed
image at the receiving end. This approach yields better compression than the DCT transform
alone at the cost of increased computational requirements.
In section 2, two approached to data compression using the zonal samples reduction
are investigated. The reconstructed data, using the first approach, has the same number of
samples as the input data. Using the second approach, the reconstructed data has a reduced
number of samples. In section 3, the procedure for the double transform technique is
presented, and the advantages of using this technique are illustrated. In section 4, a more
general results are obtained, using the data of several contours as the input to the presented
technique of data compression. Section 5, concludes the paper.
2. DATA COMPRESSION USING ZONAL SAMPLING SCHEME
In this section two approaches are investigated. The first approach, retain the first M
spectral coefficient and set the rest to zero. The number of retained spectral coefficients is
calculated as follows :
M = Round( N/n) (1)
where n is the coefficients retained factor.
N is the number of data samples.
In the second approach, the first M spectral coefficient are retained and the rest are
discarded, to form a spectrum with only ( M ) coefficients. Using this approach, the
reconstructed signal has a reduced number of samples.
For both approaches of zonal sampling, the forward transformation is as follows :
[ S(N) ] = [ T(N) ] [ X(N) ] (2)
The inverse transform, for the first approach of zonal sampling, is obtained by :
s i for i = 0 to M - 1
[ S ′(N) ] = (3)
0 for i = M to N - 1
[ X ′( N ) ]= [ IT(N) ] [ S ′( N ) ] (4)
and for the second approach
[ S(M) ] = si for i = 0 to M-1 (5)
[ X ′( M ) ] = [ IT(M) ] [ S(M)] (6)
where [ X(N) ] is the input data.
[ X ′( N ) ]is the reconstructed data.
[ X ′( M ) ] is the reconstructed data with reduced number of samples.
[ S(N) ] is the spectrum of coefficients.
[ S ′(N) ] is the spectrum of coefficients with only (M) nonzero components.
[ S(M) ] is the spectrum of coefficients with only (M) components.
[ T(N) ] is the forward transform matrix.
[ IT(N) ] is the inverse transform matrix.
3. To compare the performance of WHT and DCT the x and y co-ordinates of the contour
shown in Fig.1a are used as the input data. The reconstructed contours, with the same number
of samples, are shown in Fig.1b and Fig.1c for the WHT and DCT respectively. These results
are obtained with compression rate of 16:1. It can be seen that the performance of the DCT is
much better than that of WHT. Fig.1d and Fig.1e show the reconstructed contours, with the
reduced number of samples, for the WHT and DCT respectively. These results are obtained
with the same compression rate used by the first approach, i.e. 16:1. It can be seen that the
performance of the DCT is more or less the same as that obtained by WHT.
(a)
(b) (c) (d) (e)
a- Original contour.
b- Reconstructed contour with MSE =16.186 using WHT by the first approach of zonal compression.
c- Reconstructed contour with MSE = 1.658 using DCT by the first approach of zonal compression.
d- Reconstructed contour with MSE =40.843 using WHT by the second approach of zonal compression.
e- Reconstructed contour with MSE =40.528 using WHT by the second approach of zonal compression.
Fig.1 a: Original contour, and c-e: Reconstructed contours using 16:1 compression rate.
3. DOUBLE TRANSFORMS IMPLEMENTATION
The double transforms technique is implemented using the following three steps
step 1 : Forward transformation. Transform the input data using WHT, and select the first M
spectral coefficients for transmission or storage.
step 2 : Spectrum conversion. Inverse transform the WHT spectrum, and then forward
transform using DCT.
step 3 : Data reconstruction. Append the DCT spectrum with ( N-M ) zeros, then inverse
transform to obtain the reconstructed data.
The reconstructed contours of Fig.1a, with a compression rate of 93.75 %, i.e. 16:1,
using the WHT, DCT, and the proposed double transformation Wal-Cos, are shown in Fig.2.a-
c respectively together with the MSE obtained by each of them.
4. a: WHT ( MSE =16.186 ) b: DCT ( MSE =1.658 ) c: Wal-Cos ( MSE =2.029 )
Fig.2 Reconstructed contour using WHT, DCT, and Wal-Cos double transform,
and compression rate of 16:1.
The block diagram in Fig.3 shows how the Walsh-Cosine double transform procedure
is arranged with respect to the transmitting and receiving ends of the data compression system.
The block diagram shows that, at the transmission end of the data compression only WHT is
used, i.e. no multiplication operations are required, and all spectral coefficients are integers for
an integer input data. The total number of multiplication operations is reduced by a factor
proportional to the compression rate.
Transmitter
Input data Transformation S1(N) Select the first
X(N) T1( N ) M spectral S1(M)
coefficients
Forward transformation and spectrum compression
Receiver
Inverse Trans. S2(M)
X(M) Transformation
IT1( M ) T2( M ) M selected
M<N spectral
Spectrum converter coefficients
Append the
S2(N) Inverse Trans.
spectrum with Reconstructed
IT2( N )
N-M zeros X(N)
Spectrum expansion and inverse transformation
where :
X(N) is an N samples input data.
X(N) is an N samples reconstructed data.
X(M) is an reduced size (M samples) reconstructed data.
T1,IT 1 are the forward and the inverse Walsh transform matrices.
T2,IT 2 are the forward and the inverse Cosine transform matrices.
S1(N),S1(M) are the normal and compressed Walsh spectrums respectively.
S2(M),S2(N) are the normal and expanded Cosine spectrums respectively.
Fig.3 Double transform data compression.
4. EXPERIMENTAL RESULTS
5. Contours extracted from their original images, shown in Fig.4, were used to test the
proposed technique. The algorithm given in [7] is used for the extraction of the contours.
Fig.4 TOP: original images ( 80x80 pixels), BOTTOM: extracted contours using FCCE algorithm.
Fig.5. shows the average MSE for reconstructing the contours of the five images of
Fig.4., using DCT, WHT, and Walsh-Cosine double transform. Different compression rates are
used, 2:1, 4:1, 8:1, and 16:1. It can be seen that Walsh-Cosine double transform give MSE
close to that obtained using DCT transform with zonal compression, and also reduce the total
number of multiplication operations comparing with the DCT transform. Because the Walsh
transform does not require any multiplications. The percentage reduction in multiplication
operations versus compression rates is shown in Fig.6. These results are true using any of the
fast algorithms for DCT [3].
5 0 .0 0
P e r c e n ta g e r e d u c tio n in m u ltip lic a tio n s
4 0 .0 0
Cosine 3 0 .0 0
Walsh
Wal_Cos
2 0 .0 0
1 0 .0 0
0 .0 0
0 .0 0 2 0 .0 0 4 0 .0 0 6 0 .0 0 8 0 .0 0 1 0 0 .0 0
C o m p r e s s io n r a t e %
Fig.5. MSE v compression rate for Fig.6. Reduction in multiplications
DCT, WHT, and Wal-Cos transformation. versus compression rate.
5. CONCLUSIONS
The obtained results show that the Walsh-Cosine double transform has the advantage
of reconstructing the input data with MSE very close to that obtained using DCT, and with
6. reduced number of multiplication operations. In addition, all spectrum elements are integers for
an integer input data, and no multiplications are required at the transmitting end. In general the
proposed scheme of data compression can be efficiently used for real time compression
applications.
REFERENCES
[1] Elliott D. F.,Rao K. R., „Fast Transforms. Algorithms, Analysis, Applications", Academic
Press, 1982.
[2] Clarke R. J., „Transform Coding of Images”, Academic Press, 1985.
[3] F. H. Belgassem, W. S. Besbas „Coding of Contours Using DCT Transform Proc. of
MeleCon 2000, Vol. II, Signal and Image Processing, CYPRUS, May 29-31, 2000,
pp.616-618.
[4] Rao K. R., Yip P., „Discrete Cosine Transform”, Academic Press, NY, 1990.
[5] Lynch T. J., „Data Compression Techniques and Applications” , Wadsworth, 1985.
[6] Ramaswamy A., Mikhael W., „Development of a Near Lossless Image Compression
Technique using Mixed Transforms", Proc. of IEEE Int. Symp. on Circuits and Systema
ISCAS, Seatle, Washington, April 1995, pp.1263-1266.
[7] Dziech A. , Besbas W. S. , „New Method for Fast Closed Contour Extraction”,
IWSSIP’97 Poznan, Poland ,May 28-30,1997, pp.203-206.