The Experts below are selected from a list of 3021 Experts worldwide ranked by ideXlab platform
Nariman Farvardin - One of the best experts on this subject based on the ideXlab platform.
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Channel{Matched Hierarchical Table{Lookup Vector Quantization for Transmission of Video Over Wireless Channels
2016Co-Authors: Si R, Nariman Farvardin, The National Science Foundation, H. JafarkhaniAbstract:We propose a channel-matched hierarchical table-lookup Vector Quantizer (CM-HTVQ) which provides some ro-bustness against the channel noise. We use a nite-state channel to model slow fading channels and propose an adaptive coding scheme to transmit a source over wire-less channels. The performance of CM-HTVQ is in gen-eral slightly inferior to that of channel-optimized Vector Quantizer (COVQ) (the performances coincide at some cases); however, the encoder complexity of CM-HTVQ is much less than the encoder complexity of COVQ. Vector quantization is a powerful tool for source coding which has been used in many speech and image coding systems [1]. The encoder of a Vector Quantizer (VQ
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channel matched hierarchical table lookup Vector quantization for transmission of video over wireless channels
International Conference on Image Processing, 1996Co-Authors: H. Jafarkhani, Nariman FarvardinAbstract:We propose a channel-matched hierarchical table-lookup Vector Quantizer (CM-HTVQ) which provides some robustness against the channel noise. We use a finite-state channel to model slowly fading channels and propose an adaptive coding scheme to transmit a source over wireless channels. The performance of CM-HTVQ is in general slightly inferior to that of channel-optimized Vector Quantizer (COVQ) (the performances coincide at some cases); however, the encoder complexity of CM-HTVQ is much less than the encoder complexity of COVQ.
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trellis based scalar Vector Quantizer for memoryless sources
IEEE Transactions on Information Theory, 1994Co-Authors: Rahul Laroia, Nariman FarvardinAbstract:The paper describes a structured Vector quantization approach for stationary memoryless sources that combines the scalar-Vector Quantizer (SVQ) ideas (Laroia and Farvardin, 1993) with trellis coded quantization (Marcellin and Fischer, 1990). The resulting Quantizer is called the trellis-based scalar-Vector Quantizer (TB-SVQ). The SVQ structure allows the TB-SVQ to realize a large boundary gain while the underlying trellis code enables it to achieve a significant portion of the total granular gain. For large block-lengths and powerful (possibly complex) trellis codes the TB-SVQ can, in principle, achieve the rate-distortion bound. As indicated by the results obtained, even for reasonable block-lengths and relatively simple trellis codes, the TB-SVQ outperforms all other fixed-rate Quantizers at reasonable complexity. >
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a structured fixed rate Vector Quantizer derived from a variable length scalar Quantizer i memoryless sources
IEEE Transactions on Information Theory, 1993Co-Authors: Rahul Laroia, Nariman FarvardinAbstract:For Pt.I see ibid., vol.39, no.3, p.851-67 (1993). The fixed-rate scalar-Vector Quantizer (SVQ) for quantizing stationary memoryless sources is extended to a specific type of Vector source in which each component is a stationary memoryless scalar subsource independent of the other components. Algorithms for the design and implementation of the original SVQ are modified to apply to this case. The resulting SVQ, referred to as the extended SVQ (ESVQ), is then used to quantize stationary sources with memory (with known autocorrelation function). Numerical results are presented for the quantization of first-order Gauss-Markov sources using this scheme. It is shown that the ESVQ-based scheme performs very close to entropy-coded transform quantization while maintaining a fixed-rate output and outperforms the fixed-rate scheme that uses scalar Lloyd-Max quantization of the transform coefficients. It is also shown that this scheme performs better than implementable Vector Quantizers, especially at high rates. >
Kenneth Rose - One of the best experts on this subject based on the ideXlab platform.
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Multistage Vector Quantizer optimization for packet networks
IEEE Transactions on Signal Processing, 2003Co-Authors: H. Khalil, Kenneth RoseAbstract:A multistage Vector Quantizer (MSVQ) based coding system is source-channel optimized for packet networks. Resilience to packet loss is enhanced by a proposed interleaving approach that ensures that a single lost packet only eliminates a subset of the Vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. The new source-channel-optimized MSVQ is tested on memoryless speech line spectral frequency (LSF) parameter quantization as well as block-based image compression. With LSF coding, a source-channel-optimized MSVQ is shown to yield gains of up to 2.0 dB in signal-to-ratio (SNR) over traditional MSVQ and to substantially enhance the robustness of packetized speech transmission. Substantial gains were also obtained in the case of block-based image compression. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels.
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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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robust Vector Quantizer design by noisy channel relaxation
IEEE Transactions on Communications, 1999Co-Authors: S Gadkari, Kenneth RoseAbstract:This article proposes a method to design a Vector Quantizer (VQ) for robust performance under noisy channel conditions. By re-optimizing the Quantizer at progressively lower levels of assumed channel noise, the design is less susceptible to poor local optima. The method is applied to: (1) channel-optimized VQ design; and (2) index assignment for a source-optimized VQ. For both problems, we demonstrate substantial performance improvements over commonly used techniques.
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Entropy-constrained tree-structured Vector Quantizer design
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1996Co-Authors: Kenneth Rose, David J. Miller, Allen GershoAbstract:Current methods for the design of pruned or unbalanced tree-structured Vector Quantizers such as the generalized Breiman-Friedman-Olshen-Stone (GBFOS) algorithm proposed in 1980 are effective, but suffer from several shortcomings. We identify and clarify issues of suboptimality including greedy growing, the suboptimal encoding rule, and the need for time sharing between Quantizers to achieve arbitrary rates. We then present the leaf-optimal tree design (LOTD) method which, with a modest increase in design complexity, alters and reoptimizes tree structures obtained from conventional procedures. There are two main advantages over existing methods. First, the optimal entropy-constrained nearest-neighbor rule is used for encoding at the leaves; second, explicit Quantizer solutions are obtained at all rates without recourse to time sharing. We show that performance improvement is theoretically guaranteed. Simulation results for image coding demonstrate that close to 1 dB reduction of distortion for a given rate can be achieved by this technique relative to the GBFOS method.
R.m. Gray - One of the best experts on this subject based on the ideXlab platform.
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a robust hidden markov gauss mixture Vector Quantizer for a noisy source
IEEE Transactions on Image Processing, 2009Co-Authors: Kyungsuk Pyun, Johan Lim, R.m. GrayAbstract:Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture Vector Quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.
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combining image compression and classification using Vector quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995Co-Authors: K L Oehler, R.m. GrayAbstract:We describe a method of combining classification and compression into a single Vector Quantizer by incorporating a Bayes risk term into the distortion measure used in the Quantizer design algorithm. Once trained, the Quantizer can operate to minimize the Bayes risk weighted distortion measure if there is a model providing the required posterior probabilities, or it can operate in a suboptimal fashion by minimizing the squared error only. Comparisons are made with other Vector Quantizer based classifiers, including the independent design of quantization and minimum Bayes risk classification and Kohonen's LVQ. A variety of examples demonstrate that the proposed method can provide classification ability close to or superior to learning VQ while simultaneously providing superior compression performance. >
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variable rate Vector quantization for speech image and video compression
IEEE Transactions on Communications, 1993Co-Authors: Tom Lookabaugh, Eve A Riskin, Philip A Chou, R.m. GrayAbstract:The performance of a Vector Quantizer can be improved by using a variable-rate code. Three variable-rate Vector quantization systems are applied to speech, image, and video sources and compared to standard Vector quantization and noiseless variable-rate coding approaches. The systems range from a simple and flexible tree-based Vector Quantizer to a high-performance, but complex, jointly optimized Vector Quantizer and noiseless code. The systems provide significant performance improvements for subband speech coding, predictive image coding, and motion-compensated video, but provide only marginal improvements for Vector quantization of linear predictive coefficients in speech and direct Vector quantization of images. Criteria are suggested for determining when variable-rate Vector quantization may provide significant performance improvement over standard approaches. >
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an algorithm for joint Vector Quantizer and halftoner design
International Conference on Acoustics Speech and Signal Processing, 1992Co-Authors: R Vander A Kam, Eve A Riskin, Philip A Chou, R.m. GrayAbstract:A design procedure for a Vector Quantizer which simultaneously performs halftoning and compression of sampled monochrome images is presented. The design method is based on the generalized Lloyd algorithm which results in a Quantizer that is locally optimal under a given weighted-squared-error distortion measure. The optimal system is approximated by means of a computationally efficient Vector halftoning algorithm. A test image encoded by this method at 0.094 b/pixel is compared with images of the same rate produced by independent compression and halftoning steps. >
Allen Gersho - One of the best experts on this subject based on the ideXlab platform.
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Entropy-constrained tree-structured Vector Quantizer design
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1996Co-Authors: Kenneth Rose, David J. Miller, Allen GershoAbstract:Current methods for the design of pruned or unbalanced tree-structured Vector Quantizers such as the generalized Breiman-Friedman-Olshen-Stone (GBFOS) algorithm proposed in 1980 are effective, but suffer from several shortcomings. We identify and clarify issues of suboptimality including greedy growing, the suboptimal encoding rule, and the need for time sharing between Quantizers to achieve arbitrary rates. We then present the leaf-optimal tree design (LOTD) method which, with a modest increase in design complexity, alters and reoptimizes tree structures obtained from conventional procedures. There are two main advantages over existing methods. First, the optimal entropy-constrained nearest-neighbor rule is used for encoding at the leaves; second, explicit Quantizer solutions are obtained at all rates without recourse to time sharing. We show that performance improvement is theoretically guaranteed. Simulation results for image coding demonstrate that close to 1 dB reduction of distortion for a given rate can be achieved by this technique relative to the GBFOS method.
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globally optimal Vector Quantizer design by stochastic relaxation
IEEE Transactions on Signal Processing, 1992Co-Authors: Kenneth Zeger, Jacques Vaisey, Allen GershoAbstract:The authors present a unified formulation and study of Vector Quantizer design methods that couple stochastic relaxation (SR) techniques with the generalized Lloyd algorithm. Two new SR techniques are investigated and compared: simulated annealing (SA) and a reduced-complexity approach that modifies the traditional acceptance criterion for simulated annealing to an unconditional acceptance of perturbations. It is shown that four existing techniques all fit into a general methodology for Vector Quantizer design aimed at finding a globally optimal solution. Comparisons of the algorithms' performances when quantizing Gauss-Markov processes, speech, and image sources are given. The SA method is guaranteed to perform in a globally optimal manner, and the SR technique gives empirical results equivalent to those of SA. Both techniques result in significantly better performance than that obtained with the generalized Lloyd algorithm. >
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Competitive learning and soft competition for Vector Quantizer design
IEEE Transactions on Signal Processing, 1992Co-Authors: E. Yair, Kenneth Zeger, Allen GershoAbstract:The authors provide a convergence analysis for the Kohonen learning algorithm (KLA) with respect to Vector Quantizer (VQ) optimality criteria and introduce a stochastic relaxation technique which produces the global minimum but is computationally expensive. By incorporating the principles of the stochastic approach into the KLA, a deterministic VQ design algorithm, the soft competition scheme (SCS), is introduced. Experimental results are presented where the SCS consistently provided better codebooks than the generalized Lloyd algorithm (GLA), even when the same computation time was used for both algorithms. The SCS may therefore prove to be a valuable alternative to the GLA for VQ design. >
T Q Nguyen - One of the best experts on this subject based on the ideXlab platform.
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wavelet based image coder with entropy constrained lattice Vector Quantizer eclvq
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 1998Co-Authors: Wonha Kim, T Q NguyenAbstract:This paper presents an algorithm that jointly optimizes a lattice Vector Quantizer (LVQ) and an entropy coder in a subband coding at all ranges of bit rate. Estimation formulas for both entropy and distortion of lattice quantized subband images are derived. From these estimates, we then develop dynamic algorithm optimizing the LVQ and entropy coder together for a given entropy rate. Compared to previously reported min-max approaches, or approaches using asymptotic distortion bounds, the approach reported here quickly designs a highly accurate optimal entropy-constrained LVQ. The corresponding wavelet-based image coder also has better coding performance compared to other subband coders that use entropy-constrained LVQ, especially at low bit rates.
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adaptive wavelet packet based image coding with optimal entropy constrained lattice Vector Quantizer eclvq
IEEE Transactions on Signal Processing, 1998Co-Authors: Wonha Kim, T Q NguyenAbstract:In this work, wavelet basis and source coding are jointly optimized, while specifying the source coding strategy as entropy-constrained lattice Vector Quantizer (ECLVQ). The presented approach differs from previous works in which the choice of wavelet basis is quasioptimal, but the Quantizer set is optimally chosen.