Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.861941
Francisco M. Garcia, I. Lourtie
In general, finite-dimensional discrete-time representations of continuous-time Gaussian transients is not complete. Such representations typically lead to suboptimal detectors, where the compromise between computational complexity and processor performance requires optimization, specially when real-time processing is mandatory. This paper proposes a procedure for the optimization of the processor parameters, using the Bhattacharyya distance to evaluate the resemblance between the original continuous-time signal and its finite dimensional discrete representation. Two different decompositions are analyzed and compared, namely the Karhunen-Loeve decomposition (KLD) and the discrete wavelet transform (DWT). It is shown that the DWT presents serious advantages when the signals to detect have a large number of important eigenvalues, which is often the case in some applications such as passive sonar.
{"title":"Efficiency of real-time Gaussian transient detectors: comparing the Karhunen-Loeve and the wavelet decompositions","authors":"Francisco M. Garcia, I. Lourtie","doi":"10.1109/ICASSP.2000.861941","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.861941","url":null,"abstract":"In general, finite-dimensional discrete-time representations of continuous-time Gaussian transients is not complete. Such representations typically lead to suboptimal detectors, where the compromise between computational complexity and processor performance requires optimization, specially when real-time processing is mandatory. This paper proposes a procedure for the optimization of the processor parameters, using the Bhattacharyya distance to evaluate the resemblance between the original continuous-time signal and its finite dimensional discrete representation. Two different decompositions are analyzed and compared, namely the Karhunen-Loeve decomposition (KLD) and the discrete wavelet transform (DWT). It is shown that the DWT presents serious advantages when the signals to detect have a large number of important eigenvalues, which is often the case in some applications such as passive sonar.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116161952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.859047
P. Ainsleigh, N. Kehtarnavaz
A novel approach is presented for characterizing transient wandering tones. These signals are segmented and approximated as time series with piecewise linear instantaneous frequency and piecewise constant amplitude. Frequency rate, center frequency, and energy features are estimated in each segment of data using chirped autocorrelations and the fractional Fourier transform. These features are tracked across segments using linear dynamical models whose parameters are estimated using an expectation-maximization algorithm. A new cross-covariance estimator for adjacent states of the dynamical model is given. The feature extraction/tracking algorithm is used to characterize a measured marine-mammal vocalization. Application of the representation algorithm to signal classification is discussed.
{"title":"Characterization of transient wandering tones by dynamic modeling of fractional-Fourier features","authors":"P. Ainsleigh, N. Kehtarnavaz","doi":"10.1109/ICASSP.2000.859047","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.859047","url":null,"abstract":"A novel approach is presented for characterizing transient wandering tones. These signals are segmented and approximated as time series with piecewise linear instantaneous frequency and piecewise constant amplitude. Frequency rate, center frequency, and energy features are estimated in each segment of data using chirped autocorrelations and the fractional Fourier transform. These features are tracked across segments using linear dynamical models whose parameters are estimated using an expectation-maximization algorithm. A new cross-covariance estimator for adjacent states of the dynamical model is given. The feature extraction/tracking algorithm is used to characterize a measured marine-mammal vocalization. Application of the representation algorithm to signal classification is discussed.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"59 26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.861929
S. Heinen, P. Vary
For speech transmission in digital land mobile telephony, effective compression algorithms have to be used to achieve a high bandwidth efficiency. Furthermore, a variety of adverse transmission effects make it necessary to employ powerful error control techniques to keep bit error rates tolerably low and thus to guarantee a high speech duality. Speech compression is designed to remove irrelevancy and redundancy from the speech signal. Yet measuring the statistical properties of speech parameters extracted by practical compression schemes shows that a considerable amount of redundancy still remains, either in terms of non-uniform distribution or due to time-correlation of parameters extracted from subsequent speech segments. In this contribution, we propose a new minimum mean square error (MMSE) decoder for block-oriented trellis codes, that is able to exploit the time-correlation of subsequent parameter sets. The decoder yields non-discrete speech parameter mean square (MS) estimates. Thus it combines two approaches to exploit residual redundancy: source controlled channel decoding (SCCD) (Hagenauer 1995) and soft bit source decoding (SBSD) (Fingscheidt and Vary 1997) in one algorithm.
{"title":"Joint source-channel MMSE-decoding of speech parameters","authors":"S. Heinen, P. Vary","doi":"10.1109/ICASSP.2000.861929","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.861929","url":null,"abstract":"For speech transmission in digital land mobile telephony, effective compression algorithms have to be used to achieve a high bandwidth efficiency. Furthermore, a variety of adverse transmission effects make it necessary to employ powerful error control techniques to keep bit error rates tolerably low and thus to guarantee a high speech duality. Speech compression is designed to remove irrelevancy and redundancy from the speech signal. Yet measuring the statistical properties of speech parameters extracted by practical compression schemes shows that a considerable amount of redundancy still remains, either in terms of non-uniform distribution or due to time-correlation of parameters extracted from subsequent speech segments. In this contribution, we propose a new minimum mean square error (MMSE) decoder for block-oriented trellis codes, that is able to exploit the time-correlation of subsequent parameter sets. The decoder yields non-discrete speech parameter mean square (MS) estimates. Thus it combines two approaches to exploit residual redundancy: source controlled channel decoding (SCCD) (Hagenauer 1995) and soft bit source decoding (SBSD) (Fingscheidt and Vary 1997) in one algorithm.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122672035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.860138
K. Reinhard, M. Niranjan
Considering the perceptual importance of phonetic transitions as minimal contextual variant units, this paper addresses the problem by modelling explicitly interphone dynamics covered in diphones. Subspace projections based on a time-constrained PCA (TC-PCA) are developed which focus on the temporal evolution. They reveal characteristic trajectories present in a low-dimensional spectral representation facilitating robust parameter estimation and simultaneously optimise the discriminant information. A matched filter design is applied to a multiple hypotheses rescoring scheme which enables operating in very low-dimensional parameter space. Using such multiple hypotheses paradigm the complementary information effectiveness of modelling explicitly inter-phone dynamics covered in diphones can be shown using the TIMIT database, resulting in improved phone error rates.
{"title":"Matched filter design for diphone subspace models","authors":"K. Reinhard, M. Niranjan","doi":"10.1109/ICASSP.2000.860138","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.860138","url":null,"abstract":"Considering the perceptual importance of phonetic transitions as minimal contextual variant units, this paper addresses the problem by modelling explicitly interphone dynamics covered in diphones. Subspace projections based on a time-constrained PCA (TC-PCA) are developed which focus on the temporal evolution. They reveal characteristic trajectories present in a low-dimensional spectral representation facilitating robust parameter estimation and simultaneously optimise the discriminant information. A matched filter design is applied to a multiple hypotheses rescoring scheme which enables operating in very low-dimensional parameter space. Using such multiple hypotheses paradigm the complementary information effectiveness of modelling explicitly inter-phone dynamics covered in diphones can be shown using the TIMIT database, resulting in improved phone error rates.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"584 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122721009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.859279
M. Rziza, A. Tamtaoui, L. Morin, D. Aboutajdine
This paper presents a new algorithm of disparity map segmentation in planar facets. The origins of this method lie in the process of dense disparity map estimation, using the dynamic programming subject to interest points previously extracted. The segmentation of this map uses the normal vector at each pixel surface. The matching of pixels between the two images by dynamic programming provides us with a scattered disparity map. So the densification of this map is achieved by matching contour points extracted between the two available images. Experiments with real images have validated our method and have clearly shown the improvement over the existing methods. The dense disparity map obtained is reliable when compared to classical methods. We also get a normal vector map segmented in contours and in homogeneous regions reflecting 3D planar facets.
{"title":"Estimation and segmentation of a dense disparity map for 3D reconstruction","authors":"M. Rziza, A. Tamtaoui, L. Morin, D. Aboutajdine","doi":"10.1109/ICASSP.2000.859279","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.859279","url":null,"abstract":"This paper presents a new algorithm of disparity map segmentation in planar facets. The origins of this method lie in the process of dense disparity map estimation, using the dynamic programming subject to interest points previously extracted. The segmentation of this map uses the normal vector at each pixel surface. The matching of pixels between the two images by dynamic programming provides us with a scattered disparity map. So the densification of this map is achieved by matching contour points extracted between the two available images. Experiments with real images have validated our method and have clearly shown the improvement over the existing methods. The dense disparity map obtained is reliable when compared to classical methods. We also get a normal vector map segmented in contours and in homogeneous regions reflecting 3D planar facets.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122490642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.861205
A. Ahmed, C. Andrieu, A. Doucet, P. Rayner
In this paper we address the problem of on-line source separation with sources modelled as mixtures of Gaussians which are linearly combined via a series of non-stationary mixing matrices. The online recovery of the sources from the observations is a non-linear statistical filtering problem that we address using state of the art particle filter methods. Simulations are presented and satisfactory results are obtained.
{"title":"On-line non-stationary ICA using mixture models","authors":"A. Ahmed, C. Andrieu, A. Doucet, P. Rayner","doi":"10.1109/ICASSP.2000.861205","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.861205","url":null,"abstract":"In this paper we address the problem of on-line source separation with sources modelled as mixtures of Gaussians which are linearly combined via a series of non-stationary mixing matrices. The online recovery of the sources from the observations is a non-linear statistical filtering problem that we address using state of the art particle filter methods. Simulations are presented and satisfactory results are obtained.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114544278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.860148
A. Al-Ani, Mohamed Deriche
A novel feature selection algorithm is derived for multi-channel data. This algorithm is a hybrid information maximisation (HIM) technique based on (1) maximising the mutual information between the input and output of a network using the infomax algorithm proposed by Linsker (1988), and (2) maximising the mutual information between outputs of different network modules using the Imax algorithm introduced by Becker (see Network Computation in Neural Systems, vol.7, p.7-31, 1996). The infomax algorithm is useful in reducing the redundancy in the output units, while the Imax algorithm is capable of selecting higher order features from the input units. In this paper, we analyse the two methods and generalise the learning procedure of the Imax algorithm to make it suitable for maximising the mutual information between multi-dimensional output units from different network modules contrary to the original Imax algorithm which only maximises mutual information between two output units. We show that the proposed HIM algorithm provides a better representation of the input compared to the original two algorithms when used separately. Finally, the HIM is evaluated with respect to biological plausibility in the case of feature selection from two-channel EEG data.
{"title":"A hybrid information maximisation (HIM) algorithm for optimal feature selection from multi-channel data","authors":"A. Al-Ani, Mohamed Deriche","doi":"10.1109/ICASSP.2000.860148","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.860148","url":null,"abstract":"A novel feature selection algorithm is derived for multi-channel data. This algorithm is a hybrid information maximisation (HIM) technique based on (1) maximising the mutual information between the input and output of a network using the infomax algorithm proposed by Linsker (1988), and (2) maximising the mutual information between outputs of different network modules using the Imax algorithm introduced by Becker (see Network Computation in Neural Systems, vol.7, p.7-31, 1996). The infomax algorithm is useful in reducing the redundancy in the output units, while the Imax algorithm is capable of selecting higher order features from the input units. In this paper, we analyse the two methods and generalise the learning procedure of the Imax algorithm to make it suitable for maximising the mutual information between multi-dimensional output units from different network modules contrary to the original Imax algorithm which only maximises mutual information between two output units. We show that the proposed HIM algorithm provides a better representation of the input compared to the original two algorithms when used separately. Finally, the HIM is evaluated with respect to biological plausibility in the case of feature selection from two-channel EEG data.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122204687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.859061
M. Izzetoglu, Tayfun Akgül, B. Onaral, N. Bilgutay
In this paper we propose a least squares deconvolution method in the wavelet domain for linear time-invariant (LTI) systems with 1/f type input signals. We model the output of the system as convolution of the impulse response and the input signal, which exhibits 1/f type spectral behavior. Our aim in solving the deconvolution problem is to estimate a filter which approximates the inverse of the impulse response, so that by applying this filter to the output data we can estimate the input signal. In order to achieve this objective, we use the wavelet transform and its properties for 1/f signals, where the logarithm of the variance of the wavelet coefficients in each stage progresses linearly. We define an error criterion in the wavelet domain whose minimization yields the optimum inverse filter. We present the error minimization algorithm and the simulation results.
{"title":"Least squares deconvolution in wavelet domain for 1/f driven LTI systems","authors":"M. Izzetoglu, Tayfun Akgül, B. Onaral, N. Bilgutay","doi":"10.1109/ICASSP.2000.859061","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.859061","url":null,"abstract":"In this paper we propose a least squares deconvolution method in the wavelet domain for linear time-invariant (LTI) systems with 1/f type input signals. We model the output of the system as convolution of the impulse response and the input signal, which exhibits 1/f type spectral behavior. Our aim in solving the deconvolution problem is to estimate a filter which approximates the inverse of the impulse response, so that by applying this filter to the output data we can estimate the input signal. In order to achieve this objective, we use the wavelet transform and its properties for 1/f signals, where the logarithm of the variance of the wavelet coefficients in each stage progresses linearly. We define an error criterion in the wavelet domain whose minimization yields the optimum inverse filter. We present the error minimization algorithm and the simulation results.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116806394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.862089
S. Riis, O. Viikki
In this paper we compare a standard HMM based recognizer to a highly parameter efficient hybrid denoted hidden neural network (HNN). The comparison was done on a speaker independent command word recognition task aimed at car hands-free applications. Monophone based HMM and HNN recognizers were initially trained on clean Wall Street Journal British English data. Evaluation of these baseline models on noisy car speech data indicated superior performance of the HMMs. After smoothing to the car environment, however, an HNN with 28k parameters provided a relative error rate reduction of 23-53% over HMMs containing 21k-168k parameters. Due to the low number of parameters in the HNNs, they have a real-time decoding complexity 2-4 times below that of comparable HMMs. The low memory and computational requirements of the HNN makes it particularly attractive for implementation on portable commercial hardware like mobile phones and personal digital assistants.
{"title":"Low complexity speaker independent command word recognition in car environments","authors":"S. Riis, O. Viikki","doi":"10.1109/ICASSP.2000.862089","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.862089","url":null,"abstract":"In this paper we compare a standard HMM based recognizer to a highly parameter efficient hybrid denoted hidden neural network (HNN). The comparison was done on a speaker independent command word recognition task aimed at car hands-free applications. Monophone based HMM and HNN recognizers were initially trained on clean Wall Street Journal British English data. Evaluation of these baseline models on noisy car speech data indicated superior performance of the HMMs. After smoothing to the car environment, however, an HNN with 28k parameters provided a relative error rate reduction of 23-53% over HMMs containing 21k-168k parameters. Due to the low number of parameters in the HNNs, they have a real-time decoding complexity 2-4 times below that of comparable HMMs. The low memory and computational requirements of the HNN makes it particularly attractive for implementation on portable commercial hardware like mobile phones and personal digital assistants.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-06-05DOI: 10.1109/ICASSP.2000.860191
L. Winger
This paper presents a fast two-dimensional inverse discrete cosine transform that adapts to compressed video source statistics to reduce execution time. iDCT algorithms for sparse blocks eliminate calculations for some zero coefficients and are implemented with quad-word parallel single-instruction-multiple-data (SIMD) multimedia instructions. It is observed that end-of-block marker value histograms vary little within single shots. An adaptive control mechanism is proposed that chooses the optimal set of iDCTs to prepare for an entire shot from its 1st frames (to reduce software overheads and penalties). This introduces no degradation of decoded video quality compared with a conventional SIMD 8/spl times/8 iDCT implemented with Intel MMX instructions. It is confirmed that execution time is reduced an additional 15% with Murata's method for 4 Mbps MPEG2 natural video. In comparison, execution time is reduced 22% with a modified version Murata's method, and by 35% with the new source adaptive method.
{"title":"Source adaptive software 2D iDCT with SIMD","authors":"L. Winger","doi":"10.1109/ICASSP.2000.860191","DOIUrl":"https://doi.org/10.1109/ICASSP.2000.860191","url":null,"abstract":"This paper presents a fast two-dimensional inverse discrete cosine transform that adapts to compressed video source statistics to reduce execution time. iDCT algorithms for sparse blocks eliminate calculations for some zero coefficients and are implemented with quad-word parallel single-instruction-multiple-data (SIMD) multimedia instructions. It is observed that end-of-block marker value histograms vary little within single shots. An adaptive control mechanism is proposed that chooses the optimal set of iDCTs to prepare for an entire shot from its 1st frames (to reduce software overheads and penalties). This introduces no degradation of decoded video quality compared with a conventional SIMD 8/spl times/8 iDCT implemented with Intel MMX instructions. It is confirmed that execution time is reduced an additional 15% with Murata's method for 4 Mbps MPEG2 natural video. In comparison, execution time is reduced 22% with a modified version Murata's method, and by 35% with the new source adaptive method.","PeriodicalId":164817,"journal":{"name":"2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128422264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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