The LBG-U Method for Vector Quantization – an Improvement over LBGInspired from Neural Networks
Neural Processing Letters
Overlap and channel errors in adaptive vector quantization for image coding
Information Sciences—Informatics and Computer Science: An International Journal
An adaptive incremental LBG for vector quantization
Neural Networks
Constrained-storage multistage vector quantization based on genetic algorithms
Pattern Recognition
Fast VQ codebook search algorithm for grayscale image coding
Image and Vision Computing
Improved batch fuzzy learning vector quantization for image compression
Information Sciences: an International Journal
A fast VQ codebook generation algorithm via pattern reduction
Pattern Recognition Letters
Adaptive data hiding for vector quantization images based on overlapping codeword clustering
Information Sciences: an International Journal
Vector quantization using the firefly algorithm for image compression
Expert Systems with Applications: An International Journal
A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach
Fuzzy Sets and Systems
An efficient prediction algorithm for image vector quantization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Asymptotically optimal block quantization
IEEE Transactions on Information Theory
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
Adaptive vector quantization with codebook updating based on locality and history
IEEE Transactions on Image Processing
Fuzzy vector quantization algorithms and their application in image compression
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Engineering Applications of Artificial Intelligence
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In this paper we propose a learning mechanism to systematically design fast fuzzy clustering-based vector quantizers. Although the utilization of fuzzy clustering in vector quantization is able to reduce the dependence on initialization, it finally obtains high computational cost. This problem has been investigated by many researchers. So far, the most widely used solution is to equip the quantizer with specialized strategies for the smooth transition from fuzzy to crisp conditions. Hereby, we propose an enhanced solution to that problem. In our contribution we combine three different learning modules. The first one concerns the reduction of the number of codewords that are affected by a specific training pattern. The second one acts to reduce the number of training patterns involved in the design process. The sequential implementation of the above two modules manages to significantly reduce the computational cost of the quantizer. However, the potential risk related to the implementation of the first module is the high probability to generate small and badly delineated clusters. To handle this problem we apply, in the third module, a novel cluster distortion equalization process, according to which the codewords of small clusters are moved to the neighborhood of large ones in order to increase their size and become more competitive, obtaining a better local minimum. The proposed algorithm is rigorously evaluated and compared to other sophisticated methods in terms of grayscale image compression.