Image compression through embedded multiwavelet transform coding

Cotronei, Mariantonia; Lazzaro, Damiana; Montefusco, Laura; Puccio, Luigia

doi:10.1109/83.821728

Cited by 44 publications

(21 citation statements)

References 12 publications

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“…Multiwavelets simultaneously possess orthogonality, short support, symmetry, and a high order of approximation through vanishing moments, that all of them are important for signal processing application [103]. The performance of multiwavelet have shown superior as compare to scalars wavelets in image classification, denoising [106] and image compression [104]. In [111], it has been shown that the multiwavelet transform has an efficient signal processing technique for the feature extraction from EEG signals in comparison with scalar wavelet.…”

Section: Multiwavelet Transformmentioning

confidence: 99%

“…These features of multiwavelets cause to better performance of multiwavelets over scalar wavelets in image processing applications. Particular applications, where multiwavelets have been found to offer superior performance over single wavelets, include signal/image classification [107,108], compression [104], and denoising [106]. The wavelet transform based features have been used for epileptic EEG signal classification and recognition [109,110].…”

Section: Multiwavelet Transformmentioning

confidence: 99%

“…It offers many theoretical and experimental advantages. For example, multiwavelets have been constructed to simultaneously possess symmetry, orthogonality, and compact support [104][105][106][107][108]. The multiwavelets have some unique characteristics that cannot be obtained with scalar wavelets.…”

Section: Multiwavelet Transformmentioning

confidence: 99%

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Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Bajaj

Pachori

2014

Intelligent Systems Reference Library

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Emotion classification based on electroencephalogram (EEG) signals is a relatively new area of research in the development of brain computer interface (BCI) system with challenging issues like induction of the emotional states and the extraction of the features in order to obtain optimum classification of human emotions. The emotion classification system based on BCI can be useful in many areas like as entertainment, education, and health care. This chapter presents a new method for human emotion classification using multiwavelet transform of EEG signals. The EEG signal contains useful information related to the different emotional states, which helps us to understand the psychology and neurology of the human brain. The features namely, ratio of the norms based measure, Shannon entropy measure, and normalized Renyi entropy measure are computed from the sub-signals generated by multiwavelet decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The classification performance of the proposed method for classification of emotions using EEG signals determined by computing the classification accuracy, ten-fold crossvalidation, and confusion matrix. The proposed method has provided classification accuracy of 84.79 % for classification of human emotions namely happy, neutral, sadness, and fear from EEG signals with Morlet wavelet kernel function of MC-LS-SVM. The audio-video stimulus has been used for inducing the emotions in EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of human emotions from EEG signals.

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Section: Multiwavelet Transformmentioning

confidence: 99%

Section: Multiwavelet Transformmentioning

confidence: 99%

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Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Bajaj

Pachori

2014

Intelligent Systems Reference Library

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“…The choice of mother wavelet depends on the type of signal. If the signal represents a function of one variable, then it will require one variable as a mother function as proposed in [13] :…”

Section: Constructing a Transformation Matrixmentioning

confidence: 99%

Image Recognition Using Combination of Discrete Multi_Wavelet and Wavenet Transform

Alfaouri¹

2008

American J. of Applied Sciences

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Focus of the method was to present image identification and labeling using new combination of dynamic transforms. This method is based on using the combination of the Discrete Multi-wavelet Transform (DMWT) and Wavenet Transform (WN). In this method, the resulting coefficients were computed by the proposed multi-wavelets transform for single-level decomposition. The low pass sub bands of the upper left corner are considered in the proposed method as a resemblance and a smaller version of the original image. The Wavenet Transform (WN) of low -low coefficients will be obtained one after another and the outcome from the band pass sub bands of the lower right corner is the feature extraction of each image to the recognition identification of the image. This method gave an excellent result: 99% for a database of 100 different images which indicate that the suggested algorithm is an excellent tool to process the database of standard pose of image. The algorithm is implemented using MATLAB programming languages version 7.

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“…In particular, it is possible to achieve certain approximation accuracy using waveforms with shorter support than with standard wavelet analysis. Once appropriate coding schemes are available, multiwavelet transforms have a strong potential to achieve good image compression [9,15]. Wavelet frames correspond to a single scaling function and several wavelets and provide redundant expansions of functions.…”

Section: Introductionmentioning

confidence: 99%

Multiwavelet Frames in Signal Space Originated From Hermite Splines

Averbuch

Zheludev

Cohen

2007

IEEE Trans. Signal Process.

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We present a method for construction of multiwavelet frames for manipulation of discrete signals. The frames are generated by three-channel perfect reconstruction oversampled multifilter banks. The design of the multifilter bank starts from a pair of interpolatory multifilters. We derive these interpolatory multifilters from the cubic Hermite splines. We use original preprocessing algorithms, which transform scalar signals into vector arrays that serve as inputs to the oversampled analysis multifilter banks. These pre-processing algorithms do not degrade the approximation accuracy of the transforms of the vectors by multifilter banks. The postprocessing algorithms convert the vector output of the synthesis multifilter banks into scalar signal. The discrete framelets, generated by the designed filter banks, are symmetric and have short support. The analysis framelets have four vanishing moments, whereas the synthesis framelets converge to Hermite splines supported on the interval [-1,1].

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Image compression through embedded multiwavelet transform coding

Cited by 44 publications

References 12 publications

Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Image Recognition Using Combination of Discrete Multi_Wavelet and Wavenet Transform

Multiwavelet Frames in Signal Space Originated From Hermite Splines

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