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Christopher F. Barnes - One of the best experts on this subject based on the ideXlab platform.
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classification using Residual Vector quantization with markov bayesian structure
Data Compression Conference, 2015Co-Authors: Syed Irteza Ali Khan, David V Anderson, Christopher F. BarnesAbstract:In this work, for a given a set of code Vector assignments to an input by a multistage Residual Vector quantizer RVQ [1], Bayesian framework is formulated to find the most probable class membership of the input. Furthermore, Markov structure is also used to improve the memory cost of the classification.
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DCC - Classification Using Residual Vector Quantization with Markov-Bayesian Structure
2015 Data Compression Conference, 2015Co-Authors: Syed Irteza Ali Khan, David V Anderson, Christopher F. BarnesAbstract:In this work, for a given a set of code Vector assignments to an input by a multistage Residual Vector quantizer RVQ [1], Bayesian framework is formulated to find the most probable class membership of the input. Furthermore, Markov structure is also used to improve the memory cost of the classification.
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target tracking using Residual Vector quantization
Digital Image Computing: Techniques and Applications, 2012Co-Authors: Salman Aslam, Christopher F. Barnes, Aaron F BobickAbstract:In this work, our goal is to track visual targets using Residual Vector quantization (RVQ). We compare our results with principal components analysis (PCA) and tree structured Vector quantization (TSVQ) based tracking. This work is significant since PCA is commonly used in the Pattern Recognition, Machine Learning and Computer Vision communities. On the other hand, TSVQ is commonly used in the Signal Processing and data compression communities. RVQ with more than two stages has not received much attention due to the difficulty in producing stable designs. In this work, we bring together these different approaches into an integrated tracking framework and show that RVQ tracking performs best according to multiple criteria over a variety of publicly available datasets. Moreover, an advantage of our approach is a learning-based tracker that builds the target model while it tracks, thus avoiding the costly step of building target models prior to tracking.
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DICTA - Target Tracking Using Residual Vector Quantization
2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012Co-Authors: Salman Aslam, Christopher F. Barnes, Aaron F BobickAbstract:In this work, our goal is to track visual targets using Residual Vector quantization (RVQ). We compare our results with principal components analysis (PCA) and tree structured Vector quantization (TSVQ) based tracking. This work is significant since PCA is commonly used in the Pattern Recognition, Machine Learning and Computer Vision communities. On the other hand, TSVQ is commonly used in the Signal Processing and data compression communities. RVQ with more than two stages has not received much attention due to the difficulty in producing stable designs. In this work, we bring together these different approaches into an integrated tracking framework and show that RVQ tracking performs best according to multiple criteria over a variety of publicly available datasets. Moreover, an advantage of our approach is a learning-based tracker that builds the target model while it tracks, thus avoiding the costly step of building target models prior to tracking.
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Using Residual Vector quantization for image content classification
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Syed Irteza Ali Khan, Christopher F. BarnesAbstract:Multistage Residual Vector quantizers (RVQ) with optimal direct sum decoder codebooks have been successfully designed and implemented for data compression. Due to its multistage structure, RVQ has the ability to densely populate the input space with voronoi cell partitions. The same design concept has yielded good results in the application of image-content classification. Furthermore, the multistage RVQ, with stage-wise codebooks, provides an opportunity to perform fine-grained feature attribution for image understanding, in general, and feature foundation data generation for natural and man-made structure recognition, in specific. In, the information at the stages of RVQ is heuristically integrated to perform class conditional pattern recognition; hence the process is not robust. Markov random field (MRF) provides a suitable Bayesian framework to integrate the information available at the various stages of RVQ to achieve optimized classification in the maximum a-posteriori sense (MAP).
Nasser M Nasrabadi - One of the best experts on this subject based on the ideXlab platform.
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Rate-constrained modular predictive Residual Vector quantization of digital images
IEEE Signal Processing Letters, 1999Co-Authors: Syed A Rizvi, Lin-cheng Wang, Nasser M NasrabadiAbstract:A novel modular coding paradigm is investigated using Residual Vector quantization (RVQ) with memory that incorporates a modular neural network Vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The Vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.
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Compression of SAR imagery using adaptive Residual Vector quantization
Visual Communications and Image Processing '97, 1997Co-Authors: Nasser M Nasrabadi, Mahesh Venkatraman, Heesung KwonAbstract:Compression of SAR imagery for battlefield digitization is discussed in this paper. THe images are first processed to separate out possible target areas. These target areas are compressed losslessly to avoid any degradation of the images. The background information which is usually necessary to establish context, is compressed using a hybrid Vector quantization algorithm. An adaptive variable rate Residual Vector quantizer is use to compress the Residual signal generated by a neural network predictor. The Vector quantizer codebooks are optimized for entropy coding using an entropy-constrained algorithm to further improve the coding performance. This constrained Vector-quantizer combination performs extremely well as suggested by the experimental results.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Finite-state Residual Vector quantization using a tree-structured competitive neural network
IEEE Transactions on Circuits and Systems for Video Technology, 1997Co-Authors: Syed A Rizvi, Nasser M NasrabadiAbstract:Finite-state Vector quantization (FSVQ) is known to give better performance than the memoryless Vector quantization (VQ). This paper presents a new FSVQ scheme, called finite-state Residual Vector quantization (FSRVQ), in which each state uses a Residual Vector quantizer (RVQ) to encode the input Vector. This scheme differs from the conventional FSVQ in that the state-RVQ codebooks encode the Residual Vectors instead of the original Vectors. A neural network predictor estimates the current block based on the four previously encoded blocks. The predicted Vector is then used to identify the current state as well as to generate a Residual Vector (the difference between the current Vector and the predicted Vector). This Residual Vector is encoded using the current state-RVQ codebooks. A major task in designing our proposed FSRVQ is the joint optimization of the next-state codebook and the state-RVQ codebooks. This is achieved by introducing a novel tree-structured competitive neural network in which the first layer implements the next-state function, and each branch of the tree implements the corresponding state-RVQ. A joint training algorithm is also developed that mutually optimizes the next-state and the state-RVQ codebooks for the proposed FSBVQ. Joint optimization of the next-state function and the state-RVQ codebooks eliminates a large number of redundant states in the conventional FSVQ design; consequently, the memory requirements are substantially reduced in the proposed FSRVQ scheme. The proposed FSRVQ can be designed for high bit rates due to its very low memory requirements and the low search complexity of the state-RVQ's. Simulation results show that the proposed FSRVQ scheme outperforms conventional FSVQ schemes both in terms of memory requirements and the visual quality of the reconstructed image. The proposed FSRVQ scheme also outperforms JPEG (the current standard for still image compression) at low bit rates.
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Entropy-constrained finite-state Residual Vector quantization : a new scheme for low bit rate coding
Visual Communications and Image Processing '96, 1996Co-Authors: Syed A Rizvi, Nasser M NasrabadiAbstract:Finite-state Vector quantization (FSVQ) is known to give a better performance than a memoryless Vector quantization (VQ). Recently, a new scheme that incorporates a finite memory into a Residual Vector quantizer (RVQ) has been developed. This scheme is referred to as finite-state RVQ (FSRVQ). FSRVQ gives better performance than the conventional FSVQ with a substantial reduction in the memory requirement. The codebook search complexity of an FSRVQ is also reduced in comparison with that of the conventional FSVQ scheme. This paper presents a new variable-rate VQ scheme called entropy-constrained finite state Residual Vector quantization (EC-FSRVQ). EC-FSRVQ is designed by incorporating a constraint on the output entropy of an FSRVQ during the design process. This scheme is intended for low bit rate applications due to its low codebook search complexity and memory requirements. Experimental results show that the EC-FSRVQ outperforms JPEG at low bit rates.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Advances in Residual Vector quantization: a review
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1996Co-Authors: Christopher F. Barnes, Syed A Rizvi, Nasser M NasrabadiAbstract:Advances in Residual Vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are examined. Predictive and finite state RVQs designed and integrated into neural-network based source coding structures are revisited. Successive approximation RVQs that achieve embedded and refinable coding are reviewed. A new type of successive approximation RVQ that varies the instantaneous block rate by using different numbers of stages on different blocks is introduced and applied to image waveforms, and a scalar version of the new Residual quantizer is applied to image subbands in an embedded wavelet transform coding system.
Syed A Rizvi - One of the best experts on this subject based on the ideXlab platform.
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Rate-constrained modular predictive Residual Vector quantization of digital images
IEEE Signal Processing Letters, 1999Co-Authors: Syed A Rizvi, Lin-cheng Wang, Nasser M NasrabadiAbstract:A novel modular coding paradigm is investigated using Residual Vector quantization (RVQ) with memory that incorporates a modular neural network Vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The Vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.
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Finite-state Residual Vector quantization using a tree-structured competitive neural network
IEEE Transactions on Circuits and Systems for Video Technology, 1997Co-Authors: Syed A Rizvi, Nasser M NasrabadiAbstract:Finite-state Vector quantization (FSVQ) is known to give better performance than the memoryless Vector quantization (VQ). This paper presents a new FSVQ scheme, called finite-state Residual Vector quantization (FSRVQ), in which each state uses a Residual Vector quantizer (RVQ) to encode the input Vector. This scheme differs from the conventional FSVQ in that the state-RVQ codebooks encode the Residual Vectors instead of the original Vectors. A neural network predictor estimates the current block based on the four previously encoded blocks. The predicted Vector is then used to identify the current state as well as to generate a Residual Vector (the difference between the current Vector and the predicted Vector). This Residual Vector is encoded using the current state-RVQ codebooks. A major task in designing our proposed FSRVQ is the joint optimization of the next-state codebook and the state-RVQ codebooks. This is achieved by introducing a novel tree-structured competitive neural network in which the first layer implements the next-state function, and each branch of the tree implements the corresponding state-RVQ. A joint training algorithm is also developed that mutually optimizes the next-state and the state-RVQ codebooks for the proposed FSBVQ. Joint optimization of the next-state function and the state-RVQ codebooks eliminates a large number of redundant states in the conventional FSVQ design; consequently, the memory requirements are substantially reduced in the proposed FSRVQ scheme. The proposed FSRVQ can be designed for high bit rates due to its very low memory requirements and the low search complexity of the state-RVQ's. Simulation results show that the proposed FSRVQ scheme outperforms conventional FSVQ schemes both in terms of memory requirements and the visual quality of the reconstructed image. The proposed FSRVQ scheme also outperforms JPEG (the current standard for still image compression) at low bit rates.
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Entropy-constrained finite-state Residual Vector quantization : a new scheme for low bit rate coding
Visual Communications and Image Processing '96, 1996Co-Authors: Syed A Rizvi, Nasser M NasrabadiAbstract:Finite-state Vector quantization (FSVQ) is known to give a better performance than a memoryless Vector quantization (VQ). Recently, a new scheme that incorporates a finite memory into a Residual Vector quantizer (RVQ) has been developed. This scheme is referred to as finite-state RVQ (FSRVQ). FSRVQ gives better performance than the conventional FSVQ with a substantial reduction in the memory requirement. The codebook search complexity of an FSRVQ is also reduced in comparison with that of the conventional FSVQ scheme. This paper presents a new variable-rate VQ scheme called entropy-constrained finite state Residual Vector quantization (EC-FSRVQ). EC-FSRVQ is designed by incorporating a constraint on the output entropy of an FSRVQ during the design process. This scheme is intended for low bit rate applications due to its low codebook search complexity and memory requirements. Experimental results show that the EC-FSRVQ outperforms JPEG at low bit rates.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Advances in Residual Vector quantization: a review
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1996Co-Authors: Christopher F. Barnes, Syed A Rizvi, Nasser M NasrabadiAbstract:Advances in Residual Vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are examined. Predictive and finite state RVQs designed and integrated into neural-network based source coding structures are revisited. Successive approximation RVQs that achieve embedded and refinable coding are reviewed. A new type of successive approximation RVQ that varies the instantaneous block rate by using different numbers of stages on different blocks is introduced and applied to image waveforms, and a scalar version of the new Residual quantizer is applied to image subbands in an embedded wavelet transform coding system.
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Entropy-constrained predictive Residual Vector quantization
Optical Engineering, 1996Co-Authors: Syed A Rizvi, Nasser M Nasrabadi, Lin-cheng WangAbstract:A major problem with a Vector-quantization-based image compression scheme is its codebook search complexity. Recently, a new Vector quantization (VQ) scheme called the predictive Residual Vector quantizer (PRVQ) was proposed, which gives performance very close to that of the predictive Vector quantizer (PVQ) with very low search complexity. This paper presents a new variable-rate VQ scheme called the entropy-constrained PRVQ (EC-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. We emphasized the design of the EC-PRVQ for bit rates ranging from 0.2 to 1.00 bits per pixel. This corresponds to compression ratios of 8 through 40, which is the range likely to be used by most of the real-life applications permitting Iossy compression. The proposed EC-PRVQ is found to give a good rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG). The robustness of the EC-PRVQ is demonstrated by encoding several test images taken from outside the training data. The EC-PRVQ not only gives better performance than JPEG, at a manageable encoder complexity, but also retains the inherent simplicity of a VQ decoder.
S.w. Mclaughlin - One of the best experts on this subject based on the ideXlab platform.
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Jointly optimized trellis-coded Residual Vector quantization
IEEE Transactions on Communications, 2001Co-Authors: M.a.u. Khan, M.j.t. Smith, S.w. MclaughlinAbstract:The union of Residual Vector quantization (RVQ) and trellis-coded Vector quantization (TCVQ) was considered by various authors where the emphasis was on the sequential design. We consider a new jointly optimized combination of RVQ and TCVQ with advantages in all categories. Necessary conditions for optimality of the jointly optimized trellis-coded Residual Vector quantizers (TCRVQ) are derived. A constrained direct sum tree structure is introduced that facilitates RVQ codebook partitioning. Simulation results for jointly optimized TCRVQ are presented for memoryless Gaussian, Laplacian, and uniform sources. The rate-distortion performance is shown to be better than RVQ and sequentially designed TCRVQ.
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ISCAS (4) - Trellis coded Residual Vector quantization with application to image coding
ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), 1Co-Authors: H.a.u. Khan, M.j.t. Smith, S.w. MclaughlinAbstract:This paper describes a Vector quantization approach that combines the Residual Vector quantization (RVQ) with trellis-coded Vector quantization (TCVQ). The resulting quantizer is referred to as trellis-coded Residual Vector quantizer (TCRVQ). An entropy-constrained (EC) version is also developed and tested on synthetic as well as real sources. Performance of the entropy-constrained version of our quantizer provides improvement over the ECRVQ (entropy-constrained Residual Vector quantization) and ECTCVQ (entropy-constrained trellis-coded Vector quantization) for a fixed level of complexity. Simulation results for image coding indicate that the new scheme achieves 0.8 dB improvement over the comparable RVQ approaches in terms of signal-to-noise ratio (PSNR).
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ICASSP - Trellis-coded Residual Vector quantization: its geometrical advantages and application to image coding
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 1Co-Authors: M.a.u. Khan, M.j.t. Smith, S.w. MclaughlinAbstract:Residual Vector quantization (RVQ), also known as multistage Vector quantization, is investigated in the context of quantization cell shapes and is found to produce oblong cell shapes and suboptimal point densities. The oblong cell shapes are partially responsible for the performance degradation of RVQ compared with Vector quantization (VQ). In an attempt to realize better cell shapes, a trellis-coded RVQ (TCRVQ) is suggested and is shown to provide optimal point densities and square cell shapes. In order to improve the performance of TCRVQ, an entropy-constrained TCRVQ (EC-TCRVQ) is designed and implemented for non-uniformly distributed sources. The simulation results indicate a performance improvement of 1.5 dB for EC-TCRVQ over entropy-constrained trellis-coded VQ. For an image coding application, we have developed conditional EC-TCRVQ, by employing adjacent Vector conditioning in addition to Residual Vector conditioning. The simulation tests show that the 8/spl times/8 CEC-TCRVQ outperforms other predictive and trellis-based VQ schemes.
M.j.t. Smith - One of the best experts on this subject based on the ideXlab platform.
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Jointly optimized trellis-coded Residual Vector quantization
IEEE Transactions on Communications, 2001Co-Authors: M.a.u. Khan, M.j.t. Smith, S.w. MclaughlinAbstract:The union of Residual Vector quantization (RVQ) and trellis-coded Vector quantization (TCVQ) was considered by various authors where the emphasis was on the sequential design. We consider a new jointly optimized combination of RVQ and TCVQ with advantages in all categories. Necessary conditions for optimality of the jointly optimized trellis-coded Residual Vector quantizers (TCRVQ) are derived. A constrained direct sum tree structure is introduced that facilitates RVQ codebook partitioning. Simulation results for jointly optimized TCRVQ are presented for memoryless Gaussian, Laplacian, and uniform sources. The rate-distortion performance is shown to be better than RVQ and sequentially designed TCRVQ.
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A fast PNN design algorithm for entropy-constrained Residual Vector quantization
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1998Co-Authors: Faouzi Kossentini, M.j.t. SmithAbstract:A clustering algorithm based on the pairwise nearest-neighbor (PNN) algorithm developed by Equitz (1989), is introduced for the design of entropy-constrained Residual Vector quantizers. The algorithm designs Residual Vector quantization codebooks by merging the pair of stage clusters that minimizes the increase in overall distortion subject to a given decrease in entropy. Image coding experiments show that the clustering design algorithm typically results in more than a 200:1 reduction in design time relative to the standard iterative entropy-constrained Residual Vector quantization algorithm while introducing only small additional distortion. Multipath searching over the sequence of merges is also investigated and shown experimentally to slightly improve rate-distortion performance. The proposed algorithm can be used alone or can he followed by the iterative algorithm to improve the reproduction quality at the same bit rate.
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necessary conditions for the optimality of variable rate Residual Vector quantizers
IEEE Transactions on Information Theory, 1995Co-Authors: F. Kossentini, M.j.t. Smith, Christopher F. BarnesAbstract:Necessary conditions for the optimality of variable-rate Residual Vector quantizers are derived, and an iterative descent algorithm based on a Lagrangian formulation is introduced for designing Residual Vector quantizers having minimum average distortion subject to an entropy constraint. Simulation results for entropy-constrained Residual Vector quantizers are presented for memoryless Gaussian, Laplacian, and uniform sources. A Gauss-Markov source is also considered. The rate-distortion performance is shown to be competitive with that of entropy-constrained Vector quantization and entropy-constrained trellis-coded quantization.
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A fast searching technique for multistage Residual Vector quantizers
IEEE Signal Processing Letters, 1994Co-Authors: F. Kossentini, M.j.t. SmithAbstract:The encoding procedure for multistage Residual Vector quantizers consists of searching a tree to find the codeword that best matches the input. Many techniques have been proposed for searching such trees, ranging from the slow and faithful to the fast and inaccurate. The present authors introduce a simple method, which they call dynamic M-search, that is both fast and effective for searching the Residual Vector quantization tree. The new method is applied to image coding, performance results are presented, and advantages are shown over conventional M-search. >
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Adaptive entropy-constrained Residual Vector quantization
IEEE Signal Processing Letters, 1994Co-Authors: F. Kossentini, M.j.t. SmithAbstract:Entropy-constrained Residual Vector quantization (EC-RVQ) is a relatively new VQ method that was shown to be capable of excellent rate-distortion performance. The paper investigates the incorporation of adaptivity into the EC-RVQ framework for application to image coding. Experimental results show that the dynamic nature of the implementation leads to a noticeable improvement in coding quality as well as improved robustness. >
