DocumentCode
939567
Title
An algorithm for uniform vector quantizer design
Author
Sayood, Khalid ; Gibson, Jerry D. ; Rost, Martin C.
Volume
30
Issue
6
fYear
1984
fDate
11/1/1984 12:00:00 AM
Firstpage
805
Lastpage
814
Abstract
A vector quantizer maps a
-dimensional vector into one of a finite set of output vectors or "points". Although certain lattices have been shown to have desirable properties for vector quantization applications, there are as yet no algorithms available in the quantization literature for building quantizers based on these lattices. An algorithm for designing vector quantizers based on the root lattices
, and
and their duals is presented. Also, a coding scheme that has general applicability to all vector quantizers is presented. A four-dimensional uniform vector quantizer is used to encode Laplacian and gamma-distributed sources at entropy rates of one and two bits/sample and is demonstrated to achieve performance that compares favorably with the rate distortion bound and other scalar and vector quantizers. Finally, an application using uniform four- and eight-dimensional vector quantizers for encoding the discrete cosine transform coefficients of an image at
bit/pel is presented, which visibly illustrates the performance advantage of vector quantization over scalar quantization.
-dimensional vector into one of a finite set of output vectors or "points". Although certain lattices have been shown to have desirable properties for vector quantization applications, there are as yet no algorithms available in the quantization literature for building quantizers based on these lattices. An algorithm for designing vector quantizers based on the root lattices
, and
and their duals is presented. Also, a coding scheme that has general applicability to all vector quantizers is presented. A four-dimensional uniform vector quantizer is used to encode Laplacian and gamma-distributed sources at entropy rates of one and two bits/sample and is demonstrated to achieve performance that compares favorably with the rate distortion bound and other scalar and vector quantizers. Finally, an application using uniform four- and eight-dimensional vector quantizers for encoding the discrete cosine transform coefficients of an image at
bit/pel is presented, which visibly illustrates the performance advantage of vector quantization over scalar quantization.Keywords
Image coding; Quantization (signal); Signal quantization; Source coding; Algorithm design and analysis; Buildings; Distortion measurement; Entropy; Laplace equations; Lattices; Random variables; Rate-distortion; Tiles; Vector quantization;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.1984.1056979
Filename
1056979
Link To Document