Fast-searching algorithm for vector quantization using projection and triangular inequality

In this paper, a new and fast-searching algorithm for vector quantization is presented. Two inequalities, one used for terminating the searching process and the other used to delete impossible codewords, are presented to reduce the distortion computations. Our algorithm makes use of a vector's...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2004-12, Vol.13 (12), p.1554-1558
Main Authors: Lai, J.Z.C., Yi-Ching Liaw
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computation
Computational efficiency
Computer Graphics
Computer science
Computer Simulation
Computing time
Data compression
Data Compression - methods
Decoding
Distortion
Encoding
Fast-search algorithm
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Mathematical analysis
Nearest neighbor searches
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
projection value
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Texture
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a new and fast-searching algorithm for vector quantization is presented. Two inequalities, one used for terminating the searching process and the other used to delete impossible codewords, are presented to reduce the distortion computations. Our algorithm makes use of a vector's features (mean value, edge strength, and texture strength) to reject many unlikely codewords that cannot be rejected by other available approaches. Experimental results show that our algorithm is superior to other algorithms in terms of computing time and the number of distortion calculations. Compared with available approaches, our method can reduce the computing time and the number of distortion computations significantly. Compared with the best method of reducing distortion computation, our algorithm can further reduce the number of distortion calculations by 29% to 58.4%. Compared with the best encoding algorithm for vector quantization, our approach also further reduces the computing time by 8% to 47.7%.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2004.837559