
HardwareLimited TaskBased Quantization
Quantization plays a critical role in digital signal processing systems....
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Vector Quantization by Minimizing KullbackLeibler Divergence
This paper proposes a new method for vector quantization by minimizing t...
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A ModuloBased Architecture for AnalogtoDigital Conversion
Systems that capture and process analog signals must first acquire them ...
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MultiScale Vector Quantization with Reconstruction Trees
We propose and study a multiscale approach to vector quantization. We d...
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Quantizers with Parameterized Distortion Measures
In many quantization problems, the distortion function is given by the E...
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Nonlinear Transform Coding
We review a class of methods that can be collected under the name nonlin...
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Adapted Decimation on Finite Frames for Arbitrary Orders of SigmaDelta Quantization
In Analogtodigital (A/D) conversion, signal decimation has been proven...
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Comparisonlimited Vector Quantization
A variation of the classic vector quantization problem is considered, in which the analogtodigital (A2D) conversion is not constrained by the cardinality of the output but rather by the number of comparators available for quantization. More specifically, we consider the scenario in which a vector quantizer of dimension d is comprised of k comparators, each receiving a linear combination of the inputs and producing zero/one when this signal is above/below a threshold. Given a distribution of the inputs and a distortion criterion, the value of the linear combinations and thresholds are to be configured so as to minimize the distortion between the quantizer input and its reconstruction. This vector quantizer architecture naturally arises in many A2D conversion scenarios in which the quantizer's cost and energy consumption are severely restricted. For this novel vector quantizer architecture, we propose an algorithm to determine the optimal configuration and provide the first performance evaluation for the case of uniform and Gaussian sources.
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