Adaptive vector quantization with fuzzy distortion measure for image coding

Suryalakshmi Pemmaraju; Sunanda Mitra; L. Rodney Long; George R. Thoma; Yao-Yang Shieh; Glenn H. Roberson

doi:10.1117/12.238494

15 April 1996 Adaptive vector quantization with fuzzy distortion measure for image coding

Suryalakshmi Pemmaraju, Sunanda Mitra, L. Rodney Long, George R. Thoma, Yao-Yang Shieh, Glenn H. Roberson

Author Affiliations +

Proceedings Volume 2707, Medical Imaging 1996: Image Display; (1996) https://doi.org/10.1117/12.238494
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

Despite the proven superiority of vector quantization (VQ) over scalar quantization (SQ) in terms of rate distortion theory, currently existing vector quantization algorithms, still, suffer from several practical drawbacks, such as codebook initialization, long search-process, and optimization of the distortion measure. We present a new adaptive vector quantization algorithm that uses a fuzzy distortion measure to find a globally optimum codebook. The generation of codebooks is facilitated by a self-organizing neural network-based clustering that eliminates adhoc assignment of the codebook size as required by standard statistical clustering. In addition, a multiresolution wavelet decomposition of the original image enhances the process of codebook generation. Preliminary results using standard monochrome images demonstrate excellent convergence of the algorithm, significant bit rate reduction, and yield reconstructed images with high visual quality and good PSNR and MSE. Extension of this adaptive VQ to color image compression is currently under investigation.

Citation Download Citation

Suryalakshmi Pemmaraju, Sunanda Mitra, L. Rodney Long, George R. Thoma, Yao-Yang Shieh, and Glenn H. Roberson "Adaptive vector quantization with fuzzy distortion measure for image coding", Proc. SPIE 2707, Medical Imaging 1996: Image Display, (15 April 1996); https://doi.org/10.1117/12.238494

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE