The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Marc Vuffray - One of the best experts on this subject based on the ideXlab platform.
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Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation
2016Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:Abstract — We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynam-ical and condensation phase transition temperatures (equiva-lently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction satuRates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method. Index Terms — Lossy source coding, Rate-Distortion bound, low-density generator matrix codes, belief propagation, decima
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1Approaching the Rate-Distortion Limit with Spatial Coupling, Belief propagation and Decimation
2016Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:Abstract—We investigate an encoding scheme for lossy com-pression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a “temperature ” directly related to the “noise level of the test-channel”. We investigate the links between the algorith-mic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation “phase transition temperatures ” (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction satuRates towards the condensation temperature; (ii) for large degrees the condensation temper-ature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method. Index Terms—Lossy source coding, Rate-Distortion bound
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Approaching the Rate-Distortion Limit with Spatial Coupling, Belief Propagation, and Decimation
IEEE Transactions on Information Theory, 2015Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a "temperature" directly related to the "noise level of the test-channel". We investigate the links between the algorithmic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation "phase transition temperatures" (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction satuRates towards the condensation temperature; (ii) for large degrees the condensation temperature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method.
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Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation
IEEE Transactions on Information Theory, 2015Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynamical and condensation phase transition temperatures (equivalently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction satuRates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method.
Vahid Aref - One of the best experts on this subject based on the ideXlab platform.
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Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation
2016Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:Abstract — We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynam-ical and condensation phase transition temperatures (equiva-lently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction satuRates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method. Index Terms — Lossy source coding, Rate-Distortion bound, low-density generator matrix codes, belief propagation, decima
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1Approaching the Rate-Distortion Limit with Spatial Coupling, Belief propagation and Decimation
2016Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:Abstract—We investigate an encoding scheme for lossy com-pression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a “temperature ” directly related to the “noise level of the test-channel”. We investigate the links between the algorith-mic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation “phase transition temperatures ” (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction satuRates towards the condensation temperature; (ii) for large degrees the condensation temper-ature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method. Index Terms—Lossy source coding, Rate-Distortion bound
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Approaching the Rate-Distortion Limit with Spatial Coupling, Belief Propagation, and Decimation
IEEE Transactions on Information Theory, 2015Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a "temperature" directly related to the "noise level of the test-channel". We investigate the links between the algorithmic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation "phase transition temperatures" (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction satuRates towards the condensation temperature; (ii) for large degrees the condensation temperature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method.
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Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation
IEEE Transactions on Information Theory, 2015Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynamical and condensation phase transition temperatures (equivalently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction satuRates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method.
Nicolas Macris - One of the best experts on this subject based on the ideXlab platform.
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Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation
2016Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:Abstract — We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynam-ical and condensation phase transition temperatures (equiva-lently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction satuRates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method. Index Terms — Lossy source coding, Rate-Distortion bound, low-density generator matrix codes, belief propagation, decima
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1Approaching the Rate-Distortion Limit with Spatial Coupling, Belief propagation and Decimation
2016Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:Abstract—We investigate an encoding scheme for lossy com-pression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a “temperature ” directly related to the “noise level of the test-channel”. We investigate the links between the algorith-mic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation “phase transition temperatures ” (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction satuRates towards the condensation temperature; (ii) for large degrees the condensation temper-ature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method. Index Terms—Lossy source coding, Rate-Distortion bound
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Approaching the Rate-Distortion Limit with Spatial Coupling, Belief Propagation, and Decimation
IEEE Transactions on Information Theory, 2015Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a "temperature" directly related to the "noise level of the test-channel". We investigate the links between the algorithmic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation "phase transition temperatures" (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction satuRates towards the condensation temperature; (ii) for large degrees the condensation temperature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method.
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Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation
IEEE Transactions on Information Theory, 2015Co-Authors: Vahid Aref, Nicolas Macris, Marc VuffrayAbstract:We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression Rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic Rate-Distortion Curve approaches the optimal Curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both Curves approach the ultimate Shannon Rate-Distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynamical and condensation phase transition temperatures (equivalently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction satuRates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of Rate-Distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method.
Eve A. Riskin - One of the best experts on this subject based on the ideXlab platform.
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Optimal bit allocation and best-basis selection for wavelet packets and TSVQ
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1999Co-Authors: J.r. Goldschneider, Eve A. RiskinAbstract:To use wavelet packets for lossy data compression, the following issues must be addressed: quantization of the wavelet subbands, allocation of bits to each subband, and best-basis selection. We present an algorithm for wavelet packets that systematically identifies all bit allocations/best-basis selections on the lower convex hull of the Rate-Distortion Curve. We demonstRate the algorithm on tree-structured vector quantizers used to code image subbands from the wavelet packet decomposition.
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ICASSP - Joint optimal bit allocation and best-basis selection for wavelet packet trees
Proceedings of the 1998 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP '98 (Cat. No.98CH36181), 1Co-Authors: J.r. Goldschneider, Eve A. RiskinAbstract:In this paper, an algorithm for wavelet packet trees that can systematically identify all bit allocations/best-basis selections on the lower convex hull of the Rate-Distortion Curve is presented. The algorithm is applied to tree-structured vector quantizers used to code image subbands that result from the wavelet packet decomposition. This method is compared to optimal bit allocation for the discrete wavelet transform.
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ICASSP - Bit allocation via recursive optimal pruning with applications to wavelet/VQ image compression
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1Co-Authors: J.r. Goldschneider, Eve A. RiskinAbstract:We address the problem of bit allocation to wavelet subbands by extending the recursive optimal pruning algorithm of Kiang, Baker, Sullivan and Chiu (see IEEE Transactions on Image Processing, vol.1, no.4, p.162-9, 1992) to bit allocation. We apply the algorithm to tree-structured vector quantizers used to code image subbands that result from the wavelet decomposition. We compare this method to the GBFOS algorithm, that is, the generalized Breiman, Friedman, Olshen, and Stone (1984) bit allocation, and show that it produces many additional bit allocations that lie close to the Rate-Distortion Curve.
Michele Covell - One of the best experts on this subject based on the ideXlab platform.
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CVPR - Full Resolution Image Compression with Recurrent Neural Networks
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017Co-Authors: George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele CovellAbstract:This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression Rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study one-shot versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%–8.8% AUC (area under the Rate-Distortion Curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitRates on the Rate-Distortion Curve on the Kodak dataset images, with and without the aid of entropy coding.
