Asymptotic performance of unrestricted polar quantizers
IEEE Transactions on Information Theory
Optimal companding vector quantization for circularly symmetric sources
Information Sciences: an International Journal
Adaptive embedding techniques for VQ-compressed images
Information Sciences: an International Journal
A path optional lossless data hiding scheme based on VQ joint neighboring coding
Information Sciences: an International Journal
Compression-unimpaired batch-image encryption combining vector quantization and index compression
Information Sciences: an International Journal
IEEE Transactions on Information Theory
Geometric source coding and vector quantization
IEEE Transactions on Information Theory
Gaussian source coding with spherical codes
IEEE Transactions on Information Theory
Asymptotically optimal block quantization
IEEE Transactions on Information Theory
Two-dimensional quantization of bivariate circularly symmetric densities
IEEE Transactions on Information Theory
Companding and random quantization in several dimensions
IEEE Transactions on Information Theory
Voronoi regions of lattices, second moments of polytopes, and quantization
IEEE Transactions on Information Theory
A note on optimal multidimensional companders (Corresp.)
IEEE Transactions on Information Theory
Multidimensional spherical coordinates quantization
IEEE Transactions on Information Theory
Multidimensional companding quantization of the Gaussian source
IEEE Transactions on Information Theory
Unrestricted multistage vector quantizers
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Analytical coding of Gaussian sources
IEEE Transactions on Information Theory
Two-stage vector quantization-lattice vector quantization
IEEE Transactions on Information Theory
Piecewise uniform vector quantizers
IEEE Transactions on Information Theory - Part 1
Information Sciences: an International Journal
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The aim of this paper is to find a quantization technique that has low implementation complexity and asymptotic performance arbitrarily close to the optimum. More specifically, it is of interest to develop a new vector quantizer design procedure for a memoryless Gaussian source that yields vector quantizers with excellent performance and the structure required for fast quantization. To achieve this, we combined a fast lattice-encoding algorithm with a geometric approach to generate a model of a geometric piecewise-uniform lattice vector quantizer. Expressions for granular distortion and the optimal number of outputs points in each region were derived. Both exact and approximative asymptotic analyses were carried out. During this process, the constant probability density function of the input signal vector was kept inside the whole region. The analysis demonstrated the existence of piecewise-constant approximations to the input-vector probability density function, which is optimal for the proposed geometric piecewise-uniform vector quantizer. The considered quantization technique is near optimal for a memoryless Gaussian source. In other words, this paper proposes a method for a near-optimum, low-complex vector quantizer design based on probability density function discretization. The presented methodology gives a signal-to-quantization noise ratio that in some cases differs from the optimum by 0.1dB or less. Improvements of the considered model in performance and complexity over some of the existing techniques are also demonstrated.