Comparison between robust and adaptive vector quantization for image compression

Wail M. Refai

doi:10.1117/12.205492

23 March 1995 Comparison between robust and adaptive vector quantization for image compression

Wail M. Refai

Author Affiliations +

Proceedings Volume 2421, Image and Video Processing III; (1995) https://doi.org/10.1117/12.205492
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

Image data compression using vector quantization (VQ) has received a lot of attention in the last decade because of its simplicity and adaptability. The performance of encoding and decoding by VQ is dependent on the available codebook. It is important to design an optimal codebook based on some training set. The codebook is optimum in the sense that the codebook tries to match all the source data (the training set), as far as possible. Hence, the design of an efficient and robust codebook is of prime importance in VQ. Also, it was proven that Neural Network (NN) is a fast alternative approach to create the codebook. Neural Network appears to be particularly well-suited for VQ applications. Most NN learning algorithms are adaptive and can be used to produce effective scheme for training VQ.

Citation Download Citation

Wail M. Refai "Comparison between robust and adaptive vector quantization for image compression", Proc. SPIE 2421, Image and Video Processing III, (23 March 1995); https://doi.org/10.1117/12.205492

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