• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.7
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• pp.1203-1212
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• 1994

This paper proposes a variable block size vector quantization based on a biorthogonal wavelet transform for image compression. An image is first decomposed with the biorthogonal wavelet transform into multiresolution image and the wavelet coefficients of the middle frequency bands are segmented using the quadtree sturcture to extract the perceptually important regions in the middle frequency bands. A sedges of middle frequency bands exist the corresponding position of high frequency bands, the complicated quadtree structure of middle frequency bands is equally applied to the high frequency bands. Therefore the overhaed information of the quadtree codes needed to segment the high frequency bands can be reduced. The segmented subblocks are encoded with the codebook designed at the each scales and directions. The simulation results showed that the proposed methods could reproduce higher quality image with bit rate reduced about 20(%) than of the preceding VQ method and sufficiently reduce the bolck effect and the edge degradation.

• Spatial Information Research
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• v.17 no.4
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• pp.455-462
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• 2009

A method to find the nearly optimal PSNR values for compression was tried to remotely sensed images. There is no rule to find the best wavelet pairs for image processing. The expected wavelet pairs following the suggested algorithm showed the optimal result for various kinds of images. Firstly, the PSNR variations with three wavelet families were analyzed. In many cases the longer wavelet filter shows the higher PSNR value, but the rate is getting less in orthogonal wavelet families. Wavelets with moderate filter length are suggested at the point of computational cost. For biorthogonal families it was hard to predict from the length of filters. Multiresolution wavelet analysis was used up to level 3 with three kinds of wavelet families. Biorthogonal wavelet family showed irregular pattern to get the maximum PSNR values, while orthogonal wavelet families showed regular pattern. In orthogonal wavelet families the nearly optimal wavelet pair can be predicted from the level 1.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.130-138
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• 2003

This paper presents a discrete wavelet transform approach for determining the beginning and end times of voltage sags. Firstly, investigations in the use of some typical mother wavelets, namely Daubechies, Symlets, Coiflets and Biorthogonal are carried out and the most appropriate mother wavelet is selected. The proposed technique is based on utilizing the maximum value of Dl (at scale 1) coefficients in multiresolution analysis (MRA) based on the discrete wavelet transform. The results are compared with other methods for determining voltage sag duration, such as the Root Mean Square (RMS) voltage and Short Time Fourier Transform (STFT) methods. It is shown that the voltage sag detection technique based on the wavelet transform is a satisfactory and reliable method for detecting voltage sags in power quality disturbance analysis.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.172-175
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• 2002

This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.301-303
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• 2000

This paper presents Kohonen neural network and wavelet transform analysis based technique for industrial peak load forecasting for the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a six-scale synthesis technique.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.425-434
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• 2011

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.224-227
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• 2004

From 1980's, the DWT(Discrete Wavelet Transform) is applied to the data/image processing. Many people use the DWT in remote sensing for diversity purposes and they are satisfied with the wavelet theory. Though the algorithm for wavelet is very diverse, many people use the standard wavelet such as Daubechies D4 wavelet and biorthogonal 9/7 wavelet. We will overview the wavelet theory for discrete form which can be applied to the image processing. First, we will introduce the basic DWT algorithm and review the wavelet algorithm: EZW (Embedded Zerotree Wavelet), SPIHT(Set Partitioning in Hierarchical Trees), Lifting scheme, Curvelet, etc. Finally, we will suggest the properties of wavelet algorithm; and wavelet filter for each image processing in remote sensing.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.4
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• pp.279-294
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• 2013

We are interested in an adaptive grid method for hyperbolic equations. A multiresolution analysis, based on a biorthogonal family of interpolating scaling functions and lifted interpolating wavelets, is used to dynamically adapt grid points according to the physical field profile in each time step. Traditional finite-difference schemes with fixed stencils produce high oscillations around sharp discontinuities. In this paper, we hybridize high-resolution schemes, which are suitable for capturing singularities, and apply a finite-difference approach to the scaling functions at non-singular points. We use a total variation diminishing Runge-Kutta method for the time integration. The computational cost is proportional to the number of points present after compression. We provide several numerical examples to verify our approach.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1731-1734
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• 2003

In this paper, we describe an approach for image denoising using the lifting construction, with the spatial adaptive wavelet transform. The adaptive lifting scheme is implemented in spatial domain to be adjusted thresholds to reduce noise. In this approach we represent adaptive characteristics of biorthogonal wavelets for choosing predictors effectively. Predict filter is changed from sample to sample according to local signal features with their vanishing moments. We in this approach have implemented and applied to image denoising by finding a relevant minimax threshold. Experimental results show that the adaptive method of denoising process is compared with existing ones, such as non-adaptive wavelet, CRF(13, 7) and SWE(13, 7) wavelets used by JPEG2000.