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Kenneth Rose - One of the best experts on this subject based on the ideXlab platform.
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Quantizer Design for exploiting common information in layered coding
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Mehdi Salehifar, Tejaswi Nanjundaswamy, Kenneth RoseAbstract:Today's diverse data consumption devices and heterogeneous network conditions require content to be coded at different quality levels. Conventional scalable coding, which generates hierarchical layers that refine quality incrementally, introduces a performance penalty, as most sources are not successively refinable at finite delays for the distortion measure employed and the combination of rates at each layer. On the other hand encoding different copies at required quality levels is clearly wasteful in resources. We previously proposed a common information based framework with a relaxed hierarchical structure to generate common and individuals bit-streams for different quality levels, to provide the flexibility of operating at points between conventional scalable coding and independent coding. In this paper we propose a Quantizer Design technique for this layered coding framework, which enables extracting common information between two quality levels with negligible performance penalty. Experimental results for Laplacian sources, which are prevalent in practical multimedia systems, substantiate the effectiveness of our proposed technique.
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ICASSP - Quantizer Design for exploiting common information in layered coding
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Mehdi Salehifar, Tejaswi Nanjundaswamy, Kenneth RoseAbstract:Today's diverse data consumption devices and heterogeneous network conditions require content to be coded at different quality levels. Conventional scalable coding, which generates hierarchical layers that refine quality incrementally, introduces a performance penalty, as most sources are not successively refinable at finite delays for the distortion measure employed and the combination of rates at each layer. On the other hand encoding different copies at required quality levels is clearly wasteful in resources. We previously proposed a common information based framework with a relaxed hierarchical structure to generate common and individuals bit-streams for different quality levels, to provide the flexibility of operating at points between conventional scalable coding and independent coding. In this paper we propose a Quantizer Design technique for this layered coding framework, which enables extracting common information between two quality levels with negligible performance penalty. Experimental results for Laplacian sources, which are prevalent in practical multimedia systems, substantiate the effectiveness of our proposed technique.
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Predictive vector Quantizer Design using deterministic annealing
IEEE Transactions on Signal Processing, 2003Co-Authors: Hosam A. Khalil, Kenneth RoseAbstract:A new approach is proposed for predictive vector Quantizer (PVQ) Design, which is inherently probabilistic, and is based on ideas from information theory and analogies to statistical physics. The approach effectively resolves three longstanding fundamental shortcomings of standard PVQ Design. The first complication is due to the PVQ prediction loop, which has a detrimental impact on the convergence and the stability of the Design procedure. The second shortcoming is due to the piecewise constant nature of the Quantizer function, which makes it difficult to optimize the predictor with respect to the overall reconstruction error. Finally, a shortcoming inherited from standard VQ Design is the tendency of the Design algorithm to terminate at a locally, rather than the globally, optimal solution. We propose a new PVQ Design approach that embeds our previous asymptotic closed-loop (ACL) approach within a deterministic annealing (DA) framework. The overall DA-ACL method profits from its two main components in a complementary way. ACL is used to overcome the first difficulty and offers the means for stable Quantizer Design as it provides an open-loop Design platform, yet allows the PVQ Design algorithm to asymptotically converge to optimization of the closed-loop performance objective. DA simultaneously mitigates or eliminates the remaining Design shortcomings. Its probabilistic framework replaces hard quantization with a differentiable expected cost function that can be jointly optimized for the predictor and Quantizer parameters, and its annealing schedule allows the avoidance of many poor local optima. Substantial performance gains over traditional methods have been achieved in the simulations.
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robust predictive vector Quantizer Design
Data Compression Conference, 2001Co-Authors: H Khalil, Kenneth RoseAbstract:The Design of predictive Quantizers generally suffers from difficulties due to the prediction loop, which have an impact on the convergence and the stability of the Design procedure. We previously proposed an asymptotically closed-loop approach to Quantizer Design for predictive coding applications, which benefits from the stability of open-loop Design while asymptotically optimizing the actual closed-loop system. In this paper, we present an enhancement to the approach where joint optimization of both predictor and Quantizer is performed within the asymptotically closed-loop framework. The proposed Design method is tested on synthetic sources (first-order Gauss and Laplacian-Markov sequences), and on natural sources, in particular, line spectral frequency parameters of speech signals.
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Predictive multistage vector Quantizer Design using asymptotic closed-loop optimization
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2001Co-Authors: H Khalil, Kenneth RoseAbstract:This correspondence builds on the asymptotic closed-loop approach to predictive vector Quantizer Design (Khalil et al. 2001), and extends it to the Design of predictive multistage vector Quantizers for low bit rate video coding. The Design approach resolves longstanding shortcomings, in particular, Design stability and empty-cell problems. Simulation results show substantial gains over traditional Design approaches.
Pramod K Varshney - One of the best experts on this subject based on the ideXlab platform.
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on Quantizer Design for distributed bayesian estimation in sensor networks
IEEE Transactions on Signal Processing, 2014Co-Authors: Aditya Vempaty, Biao Chen, Pramod K VarshneyAbstract:We consider the problem of distributed estimation under the Bayesian criterion and explore the Design of optimal Quantizers in such a system. We show that, for a conditionally unbiased and efficient estimator at the fusion center and when local observations have identical distributions, it is optimal to partition the local sensors into groups, with all sensors within a group using the same quantization rule. When all the sensors use identical number of decision regions, use of identical Quantizers at the sensors is optimal. When the network is constrained by the capacity of the wireless multiple access channel over which the sensors transmit their quantized observations, we show that binary Quantizers at the local sensors are optimal under certain conditions. Based on these observations, we address the location parameter estimation problem and present our optimal Quantizer Design approach. We also derive the performance limit for distributed location parameter estimation under the Bayesian criterion and find the conditions when the widely used threshold Quantizer achieves this limit. We corroborate this result using simulations. We then relax the assumption of conditionally independent observations and derive the optimality conditions of Quantizers for conditionally dependent observations. Using counter-examples, we also show that the previous results do not hold in this setting of dependent observations and, therefore, identical Quantizers are not optimal. Index Terms
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On Quantizer Design for Distributed Bayesian Estimation in Sensor Networks
IEEE Transactions on Signal Processing, 2014Co-Authors: Aditya Vempaty, Biao Chen, Pramod K VarshneyAbstract:We consider the problem of distributed estimation under the Bayesian criterion and explore the Design of optimal Quantizers in such a system. We show that, for a conditionally unbiased and efficient estimator at the fusion center and when local observations have identical distributions, it is optimal to partition the local sensors into groups, with all sensors within a group using the same quantization rule. When all the sensors use identical number of decision regions, use of identical Quantizers at the sensors is optimal. When the network is constrained by the capacity of the wireless multiple access channel over which the sensors transmit their quantized observations, we show that binary Quantizers at the local sensors are optimal under certain conditions. Based on these observations, we address the location parameter estimation problem and present our optimal Quantizer Design approach. We also derive the performance limit for distributed location parameter estimation under the Bayesian criterion and find the conditions when the widely used threshold Quantizer achieves this limit. We corroborate this result using simulations. We then relax the assumption of conditionally independent observations and derive the optimality conditions of Quantizers for conditionally dependent observations. Using counter-examples, we also show that the previous results do not hold in this setting of dependent observations and, therefore, identical Quantizers are not optimal.Comment: 15 pages, 3 figures, submitted to IEEE Transactions on Signal Processin
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Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks
arXiv: Applications, 2013Co-Authors: Sijia Liu, Engin Masazade, Xiaojing Shen, Pramod K VarshneyAbstract:In this paper, we investigate the problem of nonmyopic (multi-step ahead) Quantizer Design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we Design the local Quantizer adaptively by solving a particlebased non-linear optimization problem which is well suited for the use of interior-point algorithm and easily embedded in the filtering process. Simulation results are provided to illustrate the effectiveness of our proposed approach.
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ACSSC - Adaptive non-myopic Quantizer Design for target tracking in wireless sensor networks
2013 Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Sijia Liu, Engin Masazade, Xiaojing Shen, Pramod K VarshneyAbstract:In this paper, we investigate the problem of non-myopic (multi-step ahead) Quantizer Design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we Design the local Quantizer adaptively by solving a particle-based non-linear optimization problem which is well suited for the use of interior-point algorithm and easily embedded in the filtering process. Simulation results are provided to illustrate the effectiveness of our proposed approach.
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Optimal Identical Binary Quantizer Design for Distributed Estimation
IEEE Transactions on Signal Processing, 2012Co-Authors: Swarnendu Kar, Hao Chen, Pramod K VarshneyAbstract:We consider the Design of identical one-bit probabilistic Quantizers for distributed estimation in sensor networks. We assume the parameter-range to be finite and known and use the maximum Cramer-Rao lower bound (CRB) over the parameter-range as our performance metric. We restrict our theoretical analysis to the class of antisymmetric Quantizers and determine a set of conditions for which the probabilistic Quantizer function is greatly simplified. We identify a broad class of noise distributions, which includes Gaussian noise in the low-SNR regime, for which the often used threshold-Quantizer is found to be minimax-optimal. Aided with theoretical results, we formulate an optimization problem to obtain the optimum minimax-CRB Quantizer. For a wide range of noise distributions, we demonstrate the superior performance of the new Quantizer-particularly in the moderate to high-SNR regime.
Yimin Mao - One of the best experts on this subject based on the ideXlab platform.
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Globally optimal vector Quantizer Design using stochastically competitive learning algorithm
Proceedings of ICSIPNN '94. International Conference on Speech Image Processing and Neural Networks, 1Co-Authors: Yimin MaoAbstract:This paper presents a learning scheme called stochastically competitive learning algorithm (SCLA) for globally optimal vector Quantizer Design. The SCLA incorporates the idea of stochastic relaxation into the on-line learning scheme of the Kohonen Learning Algorithm (KLA). The key of the SCLA is to replace the Euclidean winner rule with the stochastic competition such that at a given instant any codevector may be updated according to a probability related with its distance to the input. With computer simulations, the effectiveness of the SCLA has been demonstrated by comparing its performance with that of the GLA. >
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Stochastically competitive learning algorithm for vector Quantizer Design
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1Co-Authors: Yimin MaoAbstract:The problem of vector Quantizer (VQ) Design for practical applications can be divided into two phases: 1) to search a globally optimal codebook using a given set of training data; and 2) to make it adaptive to the new signals outside the set. The most widely used technique for VQ Design is the generalized Lloyd algorithm (GLA), while the Kohonen learning algorithm (KLA) is a very promising alternative due to its inherently adaptive capability. However, both the GLA and KLA tend to get trapped into poor local optima due to their "greedy" nature in the search process. By incorporating the principle of stochastic relaxation into the KLA, we propose a stochastically competitive learning algorithm (SCLA), which will approach the global optimum regardless of the initial configuration due to its capability of "pulling" itself from local optima. Based on the SCLA, a coding scheme is then outlined in detail to Design a codebook both globally optimal for a given set of training data and adaptive to new data outside the set. >
Antonio Ortega - One of the best experts on this subject based on the ideXlab platform.
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Quantizer Design for Energy-Based Source Localization in Sensor Networks
IEEE Transactions on Signal Processing, 2011Co-Authors: Yoon Hak Kim, Antonio OrtegaAbstract:We consider energy-based source localization applications, where distributed sensors quantize acoustic signal energy readings and transmit quantized data to a fusion node, which then produces an estimate of the source location. We propose an iterative Quantizer Design algorithm that allows us to take into account localization accuracy for Quantizer Design in the framework of the generalized Lloyd algorithm. Since source coding methodologies based on the Lloyd algorithm suffer from the presence of numerous poor local optima depending on initialization of Quantizers, we introduce an efficient initialization, the equally distance-divided Quantizer (EDQ), Designed so that Quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations demonstrate that improved performance over traditional Quantizer Designs can be achieved by using our proposed application specific strategy.
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Reduced-complexity deterministic annealing for vector Quantizer Design
EURASIP Journal on Advances in Signal Processing, 2005Co-Authors: Kemal Demirciler, Antonio OrtegaAbstract:This paper presents a reduced-complexity deterministic annealing (DA) approach for vector Quantizer (VQ) Design by using soft information processing with simplified assignment measures. Low-complexity distributions are Designed to mimic the Gibbs distribution, where the latter is the optimal distribution used in the standard DA method. These low-complexity distributions are simple enough to facilitate fast computation, but at the same time they can closely approximate the Gibbs distribution to result in near-optimal performance. We have also derived the theoretical performance loss at a given system entropy due to using the simple soft measures instead of the optimal Gibbs measure. We use the derived result to obtain optimal annealing schedules for the simple soft measures that approximate the annealing schedule for the optimal Gibbs distribution. The proposed reduced-complexity DA algorithms have significantly improved the quality of the final codebooks compared to the generalized Lloyd algorithm and standard stochastic relaxation techniques, both with and without the pairwise nearest neighbor (PNN) codebook initialization. The proposed algorithms are able to evade the local minima and the results show that they are not sensitive to the choice of the initial codebook. Compared to the standard DA approach, the reduced-complexity DA algorithms can operate over 100 times faster with negligible performance difference. For example, for the Design of a 16-dimensional vector Quantizer having a rate of 0.4375 bit/sample for Gaussian source, the standard DA algorithm achieved 3.60 dB performance in 16 483 CPU seconds, whereas the reduced-complexity DA algorithm achieved the same performance in 136 CPU seconds. Other than VQ Design, the DA techniques are applicable to problems such as classification, clustering, and resource allocation.
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IPSN - Quantizer Design and distributed encoding algorithm for source localization in sensor networks
IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks 2005., 2005Co-Authors: Antonio OrtegaAbstract:In this paper, we propose a Quantizer Design algorithm that is optimized for source localization in sensor networks. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that this goal can be achieved more efficiently when "application-specific" Quantizers are used. Our proposed Quantizer Design algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We also propose a distributed encoding algorithm that is applied after quantization and achieves rate savings by merging quantization bins without any degradation of localization performance. The merging technique in the encoding algorithm exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these algorithms to a system where an acoustic sensor model is employed for localization. For this case, we introduce the equally distance-divided Quantizer (EDQ), Designed so that Quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our Quantizer over traditional Quantizer Designs. In addition, they show rate savings (32.8%, 5 nodes, 4 bits per node) when our novel bin-merging algorithms are used. Our results also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.
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Quantizer Design and distributed encoding algorithm for source localization in sensor networks
IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks 2005., 2005Co-Authors: Antonio OrtegaAbstract:In this paper, we propose a Quantizer Design algorithm that is optimized for source localization in sensor networks. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that this goal can be achieved more efficiently when "application-specific" Quantizers are used. Our proposed Quantizer Design algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We also propose a distributed encoding algorithm that is applied after quantization and achieves rate savings by merging quantization bins without any degradation of localization performance. The merging technique in the encoding algorithm exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these algorithms to a system where an acoustic sensor model is employed for localization. For this case, we introduce the equally distance-divided Quantizer (EDQ), Designed so that Quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our Quantizer over traditional Quantizer Designs. In addition, they show rate savings (32.8%, 5 nodes, 4 bits per node) when our novel bin-merging algorithms are used. Our results also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.
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Quantizer Design for source localization in sensor networks
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Yoon Hak Kim, Antonio OrtegaAbstract:In this paper, we propose a Quantizer Design algorithm that is optimized for source localization in sensor networks. For these applications, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that to achieve this goal requires the use of "application-specific" Quantizers. Our proposed Quantizer Design algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We apply this algorithm to a system where an acoustic sensor model is employed for localization. For this case we introduce the equally distance-divided Quantizer (EDQ), Designed so that Quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our Quantizer over traditional Quantizer Designs. They also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.
Gang Feng - One of the best experts on this subject based on the ideXlab platform.
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Robust vector Quantizer Design using self-organizing neural networks
Signal Processing, 2000Co-Authors: Seyed Bahram Zahir Azami, Gang FengAbstract:Abstract In this paper we propose a new method to Design vector Quantizers for noisy channels. Self-organizing neural networks are known for their efficiency in voice and image data compression; we use self-organizing algorithm to create a topological similarity between the input space and the index space. This similarity reduces the effect of channel noise because any single bit error in a transmitted index will be translated to a close codevector in the input space which yields relatively small distortion. For an 8-bit vector Quantizer, the proposed system resulted in 4.59 dB spectral distortion in a highly noisy channel while a simple LBG, LBG with splitting and 2-D self-organizing map (SOM) resulted in 5.96, 5.46 and 5.02 dB of distortion, respectively.
