The paper presents an improved Vector Quantization (VQ) approach for discrete Hidden Markov Models (HMMs). This improved VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique, that we named the Distributed Vector Quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic microstructure and phonetic macro-structure when the estimation...

Lattice vector quantization (LVQ) reduces coding complexity and computation due to its regular structure. A new multistage LVQ (MLVQ) using an adaptive subband thresholding technique is presented and applied to image compression. The technique concentrates on reducing the quantization error of the quantized vectors by “blowing out” the residual quantization errors with an LVQ scale factor. The ...

Steganography techniques are useful to convey hidden information using various types of typically-transmitted multimedia data as cover file to conceal communication. When using JPEG as a cover file, normally message is hidden in the AC values of quantized DCT coefficients. However, concealment of message in quantization table is yet to be done. In this paper, a novel in-DQT technique for messag...

We describe the quantum mechanical scattering of slowly moving maximally charged black holes. Our technique is to develop a canonical quantization procedure on the parameter space of possible static classical solutions. With this, we compute the capture cross sections for the scattering of two black holes. Finally, we discuss how quantization on this parameter space relates to quantization of t...

Vector Quantization can be considered as a data compression technique. In the last few years, vector quantization has been increasingly applied to reduce problem complexity like pattern recognition. In speech recognition, discrete systems are developed to build up real-time systems. This paper presents original results by comparing the KMeans and the Kohonen approaches on the same recognition p...

Biogeography based optimization (BBO) is a type of evolutionary algorithm. It is a population based optimization algorithm and provides clarification about the changing distribution of all species in different environment with time. Color quantization is the process of reducing the number of colors in the image and preserving the most important color information and compromise with other. A col...

This paper introduces a new lattice quantization scheme, the multiple-scale lattice vector quantization (MSLVQ), based on the truncation of the D 10 lattice. The codebook is composed of several copies of the truncated lattice scaled with different scaling factors. A fast nearest neighbor search is introduced. We compare the performance of predictive MSLVQ for quantization of LSF coefficients wi...

This work presents a quantization technique for LSP parameters which results in a low bit-rate transmission while providing protection against channel errors. As a generalization of the so called Channel Optimized Vector Quantization (COVQ), Channel Optimized Matrix Quantization (COMQ) can remove intraframe and interframe LSP redundancy with the target of protecting the information sent through...

Semiclassical quantization methods have been successfully applied to the hydrogen atom in a magnetic field at low excitation energies where the classical dynamics is near integrable or weakly chaotic, and at high energies far above the field-free ionization threshold where the classical dynamics is completely hyperbolic. However, the energy region around E = 0 has so far resisted any attempts f...

The maximum intercluster distance and the maximum quantization error that are minimized by the MinMax algorithm are shown to be inappropriate error measures for color image quantization. A fast and effective (improves image quality) method for generalizing activity weighting to any histogram-based color quantization algorithm is presented. A new non-hierarchical color quantization technique cal...