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15 April 1996 Adaptive vector quantization with fuzzy distortion measure for image coding
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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