Results 1 
3 of
3
Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure
 in Proc. IEEE Int. Symp. on Information Theory, p. 55
, 1997
"... Gersho's bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki and Gray generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the dist ..."
Abstract

Cited by 31 (3 self)
 Add to MetaCart
(Show Context)
Gersho's bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki and Gray generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortion is uniquely determined by the vector quantization error, i.e., the Euclidean difference between the original vector and the codeword into which it is quantized. We generalize these asymptotic bounds to inputweighted quadratic distortion measures, a class of distortion measure often used for perceptually meaningful distortion. The generalization involves a more rigorous derivation of a fixed rate result of Gardner and Rao and a new result for variable rate codes. We also consider the problem of source mismatch, where the quantizer is designed using a probability density different from the true source density. The resulting asymptotic performance in terms of distortion increase in dB is shown...
Uniform quantization of random processes
"... Quantization of a continuousvalue signal into discrete form (or discretization of amplitude) is a standard task in all analog/digital devices. We consider quantization of a signal (or random process) in a probabilistic framework. The quantization method presented in this paper can be applied to sig ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Quantization of a continuousvalue signal into discrete form (or discretization of amplitude) is a standard task in all analog/digital devices. We consider quantization of a signal (or random process) in a probabilistic framework. The quantization method presented in this paper can be applied to signal coding and storage capacity problems. In order to demonstrate the general approach, the uniform quantization of a Gaussian process is studied in more detail. We investigate asymptotic properties of some accuracy characteristics, such as rate and distortion, in terms of correlation structure of the original random process when quantization cellwidth tends to zero. Some examples and numerical experiments are presented.
Quantization of random sequences and related statistical problems
"... We consider quantization of signals in probabilistic framework. In practice, signals (or random processes) are observed at sampling points. We study probabilistic models for runlength encoding (RLE) method. This method is characterized by the compression efficiency coefficient (or quantization ra ..."
Abstract
 Add to MetaCart
We consider quantization of signals in probabilistic framework. In practice, signals (or random processes) are observed at sampling points. We study probabilistic models for runlength encoding (RLE) method. This method is characterized by the compression efficiency coefficient (or quantization rate) and is widely used, for example, in digital signal and image compression. Some properties of RLE quantization rate are investigated. Statistical inference for mean RLE quantization rate is considered. In particular, the asymptotical normality of mean RLE quantization rate estimates is studied. Numerical experiments demonstrating the rate of convergence in the obtained asymptotical results are presented.