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Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
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Cited by 700 (12 self)
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The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modulation systems, especially in the 1948 paper of Oliver, Pierce, and Shannon. Also in 1948, Bennett published the first highresolution analysis of quantization and an exact analysis of quantization noise for Gaussian processes, and Shannon published the beginnings of rate distortion theory, which would provide a theory for quantization as analogtodigital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising techniques for quantization.
Statistical theory of quantization
 IEEE Transactions on Instrumentation and Measurement
, 1996
"... ..."
Sequential Signal Encoding from Noisy Measurements Using Quantizers with Dynamic Bias Control
 IEEE Transactions on Information Theory
, 2001
"... Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an informationbearing signal from lowbandwidth digitized information receive ..."
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Cited by 29 (1 self)
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Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an informationbearing signal from lowbandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedbackbased control inputs, resulting in a hierarchy of systems that trade performance for complexity.
Transform Coding with Backward Adaptive Updates
, 2000
"... The KarhunenLoeve transform (KLT) is optimal for transform coding of a Gaussian source. This is established for all scale invariant quantizers, generalizing previous results. A backward adaptive technique for combating the datadependence of the KLT is proposed and analyzed. When the adapted trans ..."
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Cited by 26 (6 self)
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The KarhunenLoeve transform (KLT) is optimal for transform coding of a Gaussian source. This is established for all scale invariant quantizers, generalizing previous results. A backward adaptive technique for combating the datadependence of the KLT is proposed and analyzed. When the adapted transform converges to a KLT, the scheme is universal among transform coders. A variety of convergence results are proven.
Adaptive Stochastic Resonance
 Proceedings of the IEEE: special issue on intelligent signal processing
, 1998
"... This paper shows how adaptive systems can learn to add an optimal amount of noise to some nonlinear feedback systems. Noise can improve the signaltonoise ratio of many nonlinear dynamical systems. This "stochastic resonance" effect occurs in a wide range of physical and biological system ..."
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Cited by 23 (11 self)
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This paper shows how adaptive systems can learn to add an optimal amount of noise to some nonlinear feedback systems. Noise can improve the signaltonoise ratio of many nonlinear dynamical systems. This "stochastic resonance" effect occurs in a wide range of physical and biological systems. The SR effect may also occur in engineering systems in signal processing, communications, and control. The noise energy can enhance the faint periodic signals or faint broadband signals that force the dynamical systems. Most SR studies assume full knowledge of a system's dynamics and its noise and signal structure. Fuzzy and other adaptive systems can learn to induce SR based only on samples from the process. These samples can tune a fuzzy system's ifthen rules so that the fuzzy system approximates the dynamical system and its noise response. The paper derives the SR optimality conditions that any stochastic learning system should try to achieve. The adaptive system learns the SR effect as the sys...
Recursive Consistent Estimation with Bounded Noise
 IEEE TRANS. INFORM. TH
, 2001
"... Estimation problems with bounded, uniformly distributed noise arise naturally in reconstruction problems from over complete linear expansions with subtractive dithered quantization. We present a simple recursive algorithm for such boundednoise estimation problems. The meansquare error (MSE) of the ..."
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Cited by 18 (12 self)
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Estimation problems with bounded, uniformly distributed noise arise naturally in reconstruction problems from over complete linear expansions with subtractive dithered quantization. We present a simple recursive algorithm for such boundednoise estimation problems. The meansquare error (MSE) of the algorithm is "almost" (1/n²), where is the number of samples. This rate is faster than the (1/n) MSE obtained by standard recursive least squares estimation and is optimal to within a constant factor.
Waveletbased Color Image Compression: Exploiting the Contrast Sensitivity Function
 IEEE Transactions on Image Processing
, 2001
"... Lossy image compression algorithms are most efficient if they suppress any invisible information and represent the visible one in a compact form. A key element of such an algorithm is therefore a metric that predicts the visibility of distortions due to quantization. For reasons of simplicity this m ..."
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Cited by 11 (1 self)
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Lossy image compression algorithms are most efficient if they suppress any invisible information and represent the visible one in a compact form. A key element of such an algorithm is therefore a metric that predicts the visibility of distortions due to quantization. For reasons of simplicity this metric is usually the meansquarederror (MSE) between the original and the compressed image. But this is a suboptimal solution and needs to be replaced by a metric based on vision models. Such a vision model is composed of several units that describe the color sensitivity, the masking phenomena and the sensitivity over spatial frequency. This paper focuses on the implementation of the latter phenomenon, that is parameterized by the contrast sensitivity function (CSF), into a codec. The discussion concerns codecs based on a discrete wavelet transformation (DWT), chosen for its similarity to the human visual system (HVS). Also the future...
The marginalized particle filter in practice
 in Proceedings of IEEE Aerospace Conference, Big Sky
, 2006
"... Positioning of moving platforms has been a technical driver for realtime applications of the particle filter (PF) in both the signal processing and the robotics communities. For this reason, we will spend some time to explain several such applications in detail, and to summarize the experiences of ..."
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Cited by 8 (8 self)
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Positioning of moving platforms has been a technical driver for realtime applications of the particle filter (PF) in both the signal processing and the robotics communities. For this reason, we will spend some time to explain several such applications in detail, and to summarize the experiences of using the PF in