Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure

Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure (1997)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Jia Li , Navin Chaddha , Robert M. Gray

Venue:	in Proc. IEEE Int. Symp. on Information Theory, p. 55
Citations:	23 - 3 self

Versions

Version History

Metadata Version 2

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure	user correction - Legacy Corrections
AUTHOR NAME	Jia Li	user correction - Legacy Corrections
AUTHOR NAME	Navin Chaddha	user correction - Legacy Corrections
AUTHOR NAME	Robert M. Gray	user correction - Legacy Corrections
ABSTRACT	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 input-weighted 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...	user correction - Legacy Corrections
YEAR	1997	INFERENCE
VENUE	in Proc. IEEE Int. Symp. on Information Theory, p. 55	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	1082--1091	INFERENCE
VOLUME	45	INFERENCE
CITATIONS	14 found	ParsCit 1.0