Adaptive learning vector quantizers for image compression

We investigate adaptive vector quantization for image compression based the idea of gold-washing. The technique is a mechanism for testing the usefulness of a code vector in a codebook. It thus provides a tool for developing new ways of creating code vectors dynamically based on the input data. In t...

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Bibliographic Details
Published in:1996 IEEE International Conference on Image Processing Proceedings Vol. 3; pp. 459 - 462 vol.3
Main Author: Jianhua Lin
Format: Conference Proceeding
Language:English
Published: IEEE 1996
Subjects:
Computed tomography
Computer science
Costs
Distortion measurement
Image coding
Impedance matching
Statistical distributions
Testing
Vector quantization
Video compression
ISBN:9780780332591, 0780332598
Online Access:Get full text
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Summary:We investigate adaptive vector quantization for image compression based the idea of gold-washing. The technique is a mechanism for testing the usefulness of a code vector in a codebook. It thus provides a tool for developing new ways of creating code vectors dynamically based on the input data. In this paper, we propose a new algorithm to quantize an input for which a close enough code vector can not be found. It guarantees that the compressed result is within pre-set distortion. We also use a learning algorithm to produce new code vectors from useful existing ones.
ISBN:9780780332591
0780332598
DOI:10.1109/ICIP.1996.560530