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At least 91 records · Page 5

On the Stochastic Quantization Method: Characteristics and Applications to Singular Systems

Introducing the generalized Langevin equation, we extend the stochastic quantization method so as to deal with singular dynamical systems beyond the ordinary territory of quantum mechanics. We also show how the uncertainty relation is built up to the quantum mechanical limit with respect to fictitious time, irrespective of its initial value, within the framework of the usual stochastic quantization method.

Kanenaga, Masahiko

Subband Image Coding with Jointly Optimized Quantizers

An iterative design algorithm for the joint design of complexity- and entropy-constrained subband quantizers and associated entropy coders is proposed. Unlike conventional subband design algorithms, the proposed algorithm does not require the use of various bit allocation algorithms. Multistage residual quantizers are employed here because they provide greater control of the complexity-performance tradeoffs, and also because they allow efficient and effective high-order statistical modeling. The resulting subband coder exploits statistical dependencies within subbands, across subbands, and across stages, mainly through complexity-constrained high-order entropy coding. Experimental results demonstrate that the complexity-rate-distortion performance of the new subband coder is exceptional.

Kossentini, Faouzi

The Visibility of DCT Quantization Noise: Spatial Frequency Summation

Computational models of the ability to detect image compression artifacts facilitate the optimization of compression parameters, such as the JPEG quantization matrix. For simplicity, some models assume that the visibility of artifacts containing different spatial frequency components is determined by the most visible component, that is, no summation over components. Using the type of noise generated by quantization in the Discrete Cosine Transform (DCT) domain, we find a degree of summation between that of probability summation and contrast energy summation.

Peterson, H. A.

Optimum quantizations

Optimum quantization, which minimizes mean square or peak error for constant number of quanta, based on probability density function of continuous variable

ERROR

Optimum quantization in dynamic systems.

Criterion for optimum design of quantizer imbedded in closed loop dynamic system is optimization of overall system performance expressed by variational criterion

QUANTIZER

Optimum quantization.

Optimum uniform quantizer structures for digital data with known continuous probability density noting telemetering of stationary Gaussian Markov process functions

Kurtenbach, A. J.