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About 10,000 papers for “Vector quantization”

Vector quantization-lattice vector quantization of speech LPC coefficients

Jianping Pan, T.R. Fischer · 2002

Two-stage vector quantization-lattice vector quantization (VQ-LVQ) is used to encode the speech line spectrum pair (LSP) parameters. VQ-LVQ has the advantages of lower implementational complexity and less required memory than split vector quantization (SVQ) and multi-stage vector quantization (MSVQ) with unstructured codebooks. Based on the authors' speech data base and the same spectral measure, VQ-LVQ can save about 3 bits/frame compared to SVQ, and has advantages of about 2 to 3 bits/frame compared to unstructured codebook MSVQ, depending on the number of stages and the survivor path search complexity. The paper also provides a discussion on some factors influencing the evaluation of the LSP encoding performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 11

Extension of two-stage vector quantization-lattice vector quantization

Jianping Pan · IEEE Transactions on Communications · 1997

This paper is the extension of two-stage vector quantization-(spherical) lattice vector quantization (VQ-(S)LVQ) recently introduced by Pan and Fischer (see IEEE Trans. Inform. Theory, vol.41, p.155, 1995). First, according to high resolution quantization theory, generalized vector quantization-lattice vector quantization (G-VQ-LVQ) is formulated in order to release the constraint of the spherical boundary for the second-stage lattice vector quantization (LVQ), which would provide possibilities of improving this kind of two-stage unstructured/structured quantizer by using more efficient LVQ. Second, among G-VQ-LVQ, vector quantization-pyramidal lattice vector quantization (VQ-PLVQ) is developed which is slightly superior or comparable to VQ-(S)LVQ in performance but has a much lower complexity. Simulation results show that for memoryless sources, VQ-PLVQ achieves a rate-distortion performance that is among the best of the fixed-rate quantization that we found in the literature. Therefore, VQ-PLVQ is an attractive alternative to VQ-(S)LVQ in practice. Third, transform VQ-PLVQ (TVQ-PLVQ) is proposed for sources with memory. For encoding 16-D vectors of the Gauss-Markov source, T-VQ-PLVQ has an advantage of close to 1.0 dB over VQ-PLVQ and is about 0.5 dB better than VQ-(S)LVQ.

Cited by 9

Partitioned Vector Quantization:

Application To Lossless, Giovanni Motta, Francesco Rizzo et al. · 2007

A novel design for a vector quantizer that uses multiple codebooks of variable dimensionality is proposed. High dimensional source vectors are first partitioned into two or more subvectors of (possibly) different length and then, each subvector is individually encoded with an appropriate codebook. Further redundancy is exploited by conditional entropy coding of the subvectors indices. This scheme allows practical quantization of high dimensional vectors in which each vector component is allowed to have different alphabet and distribution. This is typically the case of the pixels representing a hyperspectral image. We present experimental results in the lossless and nearlossless encoding of such images. The method can be easily adapted to lossy coding.

Cited by 0

Greedy vector quantization

Harald Luschgy, Gilles Pagès · arXiv (Cornell University) · 2014

We investigate the greedy version of the $L^p$-optimal vector quantization problem for an $\mathbb{R}^d$-valued random vector $X\!\in L^p$. We show the existence of a sequence $(a_N)_{N\ge 1}$ such that $a_N$ minimizes $a\mapsto\big \|\min_{1\le i\le N-1}|X-a_i|\wedge |X-a|\big\|_{L^p}$ ($L^p$-mean quantization error at level $N$ induced by $(a_1,\ldots,a_{N-1},a)$). We show that this sequence produces $L^p$-rate optimal $N$-tuples $a^{(N)}=(a_1,\ldots,a_{_N})$ ($i.e.$ the $L^p$-mean quantization error at level $N$ induced by $a^{(N)}$ goes to $0$ at rate $N^{-\frac 1d}$). Greedy optimal sequences also satisfy, under natural additional assumptions, the distortion mismatch property: the $N$-tuples $a^{(N)}$ remain rate optimal with respect to the $L^q$-norms, $p\le q

Cited by 0Open access

Network Vector Quantization

Michael Fleming, Qian Zhao, Michelle Effros · IEEE Transactions on Information Theory · 2004

We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples.

Cited by 102

Optimization of vector quantization in Hybrid Vector Scalar Quantization (HVSQ)

Dheeraj B. Varambally · ThinkTech (Texas Tech University) · 2005

The advancement in fields like multimedia, medical imagery and emergence of high resolution digital cameras has necessitated the acquisition, storage and transmission of high resolution digital images. Storage and transmission of such images are expensive in terms of bytes and bandwidth. There is a need for compressing these images to curtail the storage and transmission budget. A bewildering variety of image compression schemes featuring new concepts and techniques have been proposed to yield superior compression quality.&#13;\n&#13;\nHybrid Vector Scalar Quantization (HVSQ) is one such novel compression scheme used for compressing high resolution cervigram image archives of the US National Library of Medicine. It is a lossy compression scheme comprising of two of the well known quantization techniques namely Vector and Scalar Quantization in the wavelet domain. This thesis focuses on implementation and optimization of the Vector Quantization module of HVSQ to yield higher image quality in terms of Peak Signal to Noise Ratio (PSNR) and lower encoding time. The thesis also focuses on evaluating the performance of Vector, Scalar and Hybrid Vector Scalar Quantization. The superior performance HVSQ is then verified by compressing a few high resolution natural images.

Cited by 0Open access