A fast VQ codebook generation algorithm using codeword displacement
Pattern Recognition
Improvement of the k-means clustering filtering algorithm
Pattern Recognition
Improvement of the fast exact pairwise-nearest-neighbor algorithm
Pattern Recognition
VQ indexes compression and information hiding using hybrid lossless index coding
Digital Signal Processing
A fast k-means clustering algorithm using cluster center displacement
Pattern Recognition
A novel encoding algorithm for vector quantization using transformed codebook
Pattern Recognition
Image and Vision Computing
Fast global k-means clustering using cluster membership and inequality
Pattern Recognition
Fast agglomerative clustering using information of k-nearest neighbors
Pattern Recognition
Fast VQ Codebook Generation Method Using Codeword Stability Check and Finite State Concept
Fundamenta Informaticae
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A finite-state vector quantizer is a finite-state machine used for data compression: Each successive source vector is encoded into a codeword using a minimum distortion rule, and into a code book, depending on the encoder state. The current state and the selected codeword then determine the next encoder state. A finite-state vector quantizer is capable of making better use of the memory in a source than is an ordinary memoryless vector quantizer of the same dimension or blocklength. Design techniques are introduced for finite-state vector quantizers that combine ad hoc algorithms with an algorithm for the design of memoryless vector quantizers. Finite-state vector quantizers are designed and simulated for Gauss-Markov sources and sampled speech data, and the resulting performance and storage requirements are compared with ordinary memoryless vector quantization.