Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship ...Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.展开更多
The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can b...The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.展开更多
At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image s...At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.展开更多
A photon structure is advanced based on the experimental evidence and the vector potential quantization at a single photon level. It is shown that the photon is neither a point particle nor an infinite wave but behave...A photon structure is advanced based on the experimental evidence and the vector potential quantization at a single photon level. It is shown that the photon is neither a point particle nor an infinite wave but behaves rather like a local “wave-corpuscle” extended over a wavelength, occupying a minimum quantization volume and guided by a non-local vector potential real wave function. The quantized vector potential oscillates over a wavelength with circular left or right polarization giving birth to orthogonal magnetic and electric fields whose amplitudes are proportional to the square of the frequency. The energy and momentum are carried by the local wave-corpuscle guided by the non-local vector potential wave function suitably normalized.展开更多
Many classical encoding algorithms of vector quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability...Many classical encoding algorithms of vector quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.展开更多
Information hiding schemes based on vector quantization (VQ) usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree ...Information hiding schemes based on vector quantization (VQ) usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization (CPTSVQ). The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.展开更多
A fast encoding algorithm based on the mean square error (MSE) distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform (WT) coefficients of images, can...A fast encoding algorithm based on the mean square error (MSE) distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform (WT) coefficients of images, can simplify the realization of the non-linear interpolated vector quantization (NLIVQ) technique and make the partial distance search (PDS) algorithm more efficient. Utilizing the relationship of vector L2-norm and its Euclidean distance, some conditions of eliminating unnecessary codewords are obtained. Further, using inequality constructed by the subvector L2-norm, more unnecessary codewords are eliminated. During the search process for code, mostly unlikely codewords can be rejected by the proposed algorithm combined with the non-linear interpolated vector quantization technique and the partial distance search technique. The experimental results show that the reduction of computation is outstanding in the encoding time and complexity against the full search method.展开更多
A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high co...A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3~0.4 dB compared to the full-search VQ.展开更多
Lattice vector quantization (LVQ) has been used for real-time speech and audio coding systems. Compared with conventional vector quantization, LVQ has two main advantages: It has a simple and fast encoding process,...Lattice vector quantization (LVQ) has been used for real-time speech and audio coding systems. Compared with conventional vector quantization, LVQ has two main advantages: It has a simple and fast encoding process, and it significantly reduces the amount of memory required. Therefore, LVQ is suitable for use in low-complexity speech and audio coding. In this paper, we describe the basic concepts of LVQ and its advantages over conventional vector quantization. We also describe some LVQ techniques that have been used in speech and audio coding standards of international standards developing organizations (SDOs).展开更多
A fast encoding algorithm was presented which made full use of two characteristics of a vector, its sum and variance. In this paper, a vector was separated into two subvectors, one is the first half of the coordinates...A fast encoding algorithm was presented which made full use of two characteristics of a vector, its sum and variance. In this paper, a vector was separated into two subvectors, one is the first half of the coordinates and the other contains the remaining coordinates. Three inequalities based on the characteristics of the sums and variances of a vector and its two subvectors were introduced to reject those codewords which are impossible to be the nearest codeword. The simulation results show that the proposed algorithm is faster than the improved equal average eaual variance nearest neighbor search (EENNS) algorithm.展开更多
This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients...This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with展开更多
Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall per...Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall performance measure. This paper proposes a new technology to design the decoder codebook, which is different from the encoder codebook to optimise the overall performance. The performance improvement is achieved with no effect on encoding complexity, both storage and time consuming, but a modest increase in storage complexity of decoder.展开更多
First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantizat...First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.展开更多
A new intrusion detection method based on learning vector quantization (LVQ) with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intr...A new intrusion detection method based on learning vector quantization (LVQ) with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages: (1) feature selection and data normalization processing;(2) learning the training data selected from the feature data set; (3) identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection.展开更多
Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.Wi...Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional展开更多
Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase ...Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase the class separability and optimize the clustering procedure. Speaker-dependent (SD) and speaker-independent (SI) experiments are performed to evaluate the performance of the proposed method. The experiment results show that the proposed method is capable of reaching the word error rate of 3.76% in SD case and 6.60 % in SI case. Such a system can be suitable for being embedded in personal digital assistant(PDA), mobile phone and so on to perform voice controlling such as digit dialing, calculating, etc.展开更多
A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages ...A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31~33 dB with a rate of 0.2 bpp.展开更多
In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is signif...In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.展开更多
Digital watermarking has been presented as a new method for copyright protection by embedding a secret signal in a digital image or video sequence. Common digital image watermarking techniques are based on the concept...Digital watermarking has been presented as a new method for copyright protection by embedding a secret signal in a digital image or video sequence. Common digital image watermarking techniques are based on the concept of spread spectrum communications, which can be classified in two catalogues: spatial domain and transform domain based. Most of transform domain watermarking methods are based on discrete cosine transforms (DCT) and robust to JPEG lossy compression. Recently, digital image watermarking based on another important lossy compression technique, vector quantization (VQ), has been presented, which carries watermark information by codeword indices. It is secret and efficient, and is robust to VQ compression with the same codebook. However, the embedded information is less and the extraction process requires the original image. This paper presents a more efficient VQ based image watermarking method, which can embed a large gray level watermark into the original image with less extra distortion and perform the watermark extraction without the original image. In addition, the proposed watermarking algorithm is very secret because two keys are required for watermark extraction. Experimental results demonstrate the effectiveness of the proposed technique.展开更多
A novel approach for near-lossless compression of Color Filtering Array (CFA) data in wireless endoscopy capsule is proposed in this paper. The compression method is based on pre-processing and vector quantization. Fi...A novel approach for near-lossless compression of Color Filtering Array (CFA) data in wireless endoscopy capsule is proposed in this paper. The compression method is based on pre-processing and vector quantization. First, the CFA raw data are low pass filtered and rearranged during pre-processing. Then, pairs of pixels are vector quantized into macros of 9 bits by applying block par-tition and index mapping in succession. These macros are entropy compressed by Joint Photographic Experts Group-Lossless Standard (JPEG-LS) finally. The complex step of codeword searching in Vector Quantization (VQ) is avoided by a predefined partition rule, which is suitable for hardware imple-mentation. By control of the pre-processor and VQ scheme, either high quality compression under un- filtered case or high ratio compression under filtered case can be realized, with the average Peak Sig-nal-to-Noise Ratio (PSNR) more than 43dB and 37dB respectively. Compared with the state-of-the-art method and the previously proposed method, our compression approach outperforms in compression performance as well as in flexibility.展开更多
基金funded by the National Science Foundation of China(62006068)Hebei Natural Science Foundation(A2021402008),Natural Science Foundation of Scientific Research Project of Higher Education in Hebei Province(ZD2020185,QN2020188)333 Talent Supported Project of Hebei Province(C20221026).
文摘Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.
基金This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401)in part by the 2022 Yeungnam University Research Grant.
文摘The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.
文摘At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.
文摘A photon structure is advanced based on the experimental evidence and the vector potential quantization at a single photon level. It is shown that the photon is neither a point particle nor an infinite wave but behaves rather like a local “wave-corpuscle” extended over a wavelength, occupying a minimum quantization volume and guided by a non-local vector potential real wave function. The quantized vector potential oscillates over a wavelength with circular left or right polarization giving birth to orthogonal magnetic and electric fields whose amplitudes are proportional to the square of the frequency. The energy and momentum are carried by the local wave-corpuscle guided by the non-local vector potential wave function suitably normalized.
文摘Many classical encoding algorithms of vector quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.
基金supported by the National Natural Science Foundation of China under Grant No.60133012 and No.661272374
文摘Information hiding schemes based on vector quantization (VQ) usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization (CPTSVQ). The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.
基金the National Natural Science Foundation of China (60602057)the NaturalScience Foundation of Chongqing Science and Technology Commission (2006BB2373).
文摘A fast encoding algorithm based on the mean square error (MSE) distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform (WT) coefficients of images, can simplify the realization of the non-linear interpolated vector quantization (NLIVQ) technique and make the partial distance search (PDS) algorithm more efficient. Utilizing the relationship of vector L2-norm and its Euclidean distance, some conditions of eliminating unnecessary codewords are obtained. Further, using inequality constructed by the subvector L2-norm, more unnecessary codewords are eliminated. During the search process for code, mostly unlikely codewords can be rejected by the proposed algorithm combined with the non-linear interpolated vector quantization technique and the partial distance search technique. The experimental results show that the reduction of computation is outstanding in the encoding time and complexity against the full search method.
文摘A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3~0.4 dB compared to the full-search VQ.
文摘Lattice vector quantization (LVQ) has been used for real-time speech and audio coding systems. Compared with conventional vector quantization, LVQ has two main advantages: It has a simple and fast encoding process, and it significantly reduces the amount of memory required. Therefore, LVQ is suitable for use in low-complexity speech and audio coding. In this paper, we describe the basic concepts of LVQ and its advantages over conventional vector quantization. We also describe some LVQ techniques that have been used in speech and audio coding standards of international standards developing organizations (SDOs).
文摘A fast encoding algorithm was presented which made full use of two characteristics of a vector, its sum and variance. In this paper, a vector was separated into two subvectors, one is the first half of the coordinates and the other contains the remaining coordinates. Three inequalities based on the characteristics of the sums and variances of a vector and its two subvectors were introduced to reject those codewords which are impossible to be the nearest codeword. The simulation results show that the proposed algorithm is faster than the improved equal average eaual variance nearest neighbor search (EENNS) algorithm.
文摘This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with
文摘Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall performance measure. This paper proposes a new technology to design the decoder codebook, which is different from the encoder codebook to optimise the overall performance. The performance improvement is achieved with no effect on encoding complexity, both storage and time consuming, but a modest increase in storage complexity of decoder.
文摘First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.
基金Supported by the National Natural Science Foundation of China (60573047), Natural Science Foundation of the Science and Technology Committee of Chongqing (8503) and the Applying Basic Research of the Education Committee of Chongqing (KJ060804)
文摘A new intrusion detection method based on learning vector quantization (LVQ) with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages: (1) feature selection and data normalization processing;(2) learning the training data selected from the feature data set; (3) identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection.
文摘Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional
文摘Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase the class separability and optimize the clustering procedure. Speaker-dependent (SD) and speaker-independent (SI) experiments are performed to evaluate the performance of the proposed method. The experiment results show that the proposed method is capable of reaching the word error rate of 3.76% in SD case and 6.60 % in SI case. Such a system can be suitable for being embedded in personal digital assistant(PDA), mobile phone and so on to perform voice controlling such as digit dialing, calculating, etc.
文摘A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31~33 dB with a rate of 0.2 bpp.
文摘In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.
文摘Digital watermarking has been presented as a new method for copyright protection by embedding a secret signal in a digital image or video sequence. Common digital image watermarking techniques are based on the concept of spread spectrum communications, which can be classified in two catalogues: spatial domain and transform domain based. Most of transform domain watermarking methods are based on discrete cosine transforms (DCT) and robust to JPEG lossy compression. Recently, digital image watermarking based on another important lossy compression technique, vector quantization (VQ), has been presented, which carries watermark information by codeword indices. It is secret and efficient, and is robust to VQ compression with the same codebook. However, the embedded information is less and the extraction process requires the original image. This paper presents a more efficient VQ based image watermarking method, which can embed a large gray level watermark into the original image with less extra distortion and perform the watermark extraction without the original image. In addition, the proposed watermarking algorithm is very secret because two keys are required for watermark extraction. Experimental results demonstrate the effectiveness of the proposed technique.
基金the National Natural Science Foundation of China (No. 60506007).
文摘A novel approach for near-lossless compression of Color Filtering Array (CFA) data in wireless endoscopy capsule is proposed in this paper. The compression method is based on pre-processing and vector quantization. First, the CFA raw data are low pass filtered and rearranged during pre-processing. Then, pairs of pixels are vector quantized into macros of 9 bits by applying block par-tition and index mapping in succession. These macros are entropy compressed by Joint Photographic Experts Group-Lossless Standard (JPEG-LS) finally. The complex step of codeword searching in Vector Quantization (VQ) is avoided by a predefined partition rule, which is suitable for hardware imple-mentation. By control of the pre-processor and VQ scheme, either high quality compression under un- filtered case or high ratio compression under filtered case can be realized, with the average Peak Sig-nal-to-Noise Ratio (PSNR) more than 43dB and 37dB respectively. Compared with the state-of-the-art method and the previously proposed method, our compression approach outperforms in compression performance as well as in flexibility.