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Topic: Vector quantization

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	Vector quantizer - Patent 4560977
		Vector quantization can be considered to be a transformation to an output vector that is the closest to (the least distorted from) the input vector X. 9 shows the arrangement of output vectors with respect to an input vector in a three-dimensional signal space (R.sub.1, G.sub.1, and B.sub.1).
		Vector quantization is essentially the partition of a multidimentional signal space into a finite number of subspaces, so in performing high-speed vector quantization according to the present invention, the signal space R.sup.k is partitioned into n (=log.sub.2 N) stages.
		Vector quantization is the mapping of a representative point y.sub.i as the output vector of the input vector X included in a specific subspace R.sub.i.
	www.freepatentsonline.com /4560977.html (8952 words)

	Method and apparatus for vector quantization by hashing - Patent 4979039
		The vector quantizer of claim 3 wherein the sample elements of each signal vector are pixels of a video signal to be compressed, the pixels of each signal vector adjoining one another in a matrix portion of a two-dimensional array of the pixels, the array corresponding to an image frame of the video signal.
		The vector quantizer of claim 4 further comprising means for averaging pixels adjoining one another in portions of an original, two-dimensional array of the pixels corresponding to the image frame of the video signal to generate a two-dimensional array of averaged pixels and wherein the hashing means operates on the averaged pixels.
		The vector quantizer of claim 27 further comprising means for averaging pixels adjoining one another in portions of an original, two-dimensional array of the pixels corresponding to the image frame of the video signal to generate a two-dimensional array of averaged pixels and wherein the hashing means operates on the averaged pixels.
	www.freepatentsonline.com /4979039.html (11591 words)

	Vector Quantization (Site not responding. Last check: 2007-10-03)
		Vector quantization is used in many applications such as image and voice compression, voice recognition (in general statistical pattern recognition), and surprisingly enough in volume rendering (I have no idea how VQ is used in volume rendering!).
		Given an input vector, the codeword that is chosen to represent it is the one in the same Voronoi region.
		The representative codeword is determined to be the closest in Euclidean distance from the input vector.
	www.geocities.com /mohamedqasem/vectorquantization/vq.html (1265 words)

	Image Compression with Vector Quantization - Games++
		Strictly speaking, quantization is the procedure of approximating continuous with discrete values; in practice, the input values to the quantization procedure are often also discrete, but with a much finer resolution than that of the output values.
		You can do vector quantization (VQ) too, replacing vectors from a continuous (or dense discrete) input set with vectors from a much sparser set (note that here by vector we mean an ordered set of N numbers, not just the special case of points in 3D space).
		The codebook vectors in the six cells adjacent to the x = y diagonal are shifted away from the density maxima in their cells, which means that the average quantization error in these cells will be unnecessarily high.
	www.gamespp.com /algorithms/imageCompressionVectorQuantization.html (1438 words)

	Quantization
		Quantization refers to the process of approximating the continuous set of values in the image data with a finite (preferably small) set of values.
		The input to a quantizer is the original data, and the output is always one among a finite number of levels.
		In scalar quantization, each input symbol is treated separately in producing the output, while in vector quantization the input symbols are clubbed together in groups called vectors, and processed to give the output.
	www.debugmode.com /imagecmp/quantize.htm (404 words)

	Vector Quantization
		Vector quantization (VQ) is a lossy data compression method based on the principle of block coding.
		In the earlier days, the design of a vector quantizer (VQ) is considered to be a challenging problem due to the need for multi-dimensional integration.
		Given a vector source with its statistical properties known, given a distortion measure, and given the number of codevectors, find a codebook (the set of all red stars) and a partition (the set of blue lines) which result in the smallest average distortion.
	www.data-compression.com /vq.html (721 words)

	Gamasutra - Features - "Image Compression with Vector Quantization" [04.16.01]
		The main problem in VQ is choosing the vectors for the codebook so that the mean quantization error is minimal; after the codebook is known, mapping input vectors to it is a trivial matter of finding the best match.
		During the former, each of the vectors from the input set is assigned to the nearest vector from the codebook.
		During the latter, each of the codebook vectors is replaced with the centroid (in this case, average) of all input vectors assigned to it.
	www.gamasutra.com /features/20010416/ivanov_02.htm (1702 words)

	Quantization (signal processing) - Wikipedia, the free encyclopedia
		A common use of quantization is in the conversion of a discrete signal (a sampled continuous signal) into a digital signal by quantizing.
		The simplest and best-known form of quantization is referred to as scalar quantization, since it operates on scalar (as opposed to multi-dimensional vector) input data.
		In many cases, quantization can be viewed as the fundamental element that distinguishes lossy data compression from lossless data compression, and the use of quantization is nearly always motivated by the need to reduce the amount of data needed to represent a signal.
	en.wikipedia.org /wiki/Quantization_(signal_processing) (1028 words)

	Learning Vector Quantization Visualization
		Learning vector quantization is a precursor of the well-known self-organizing maps (also called Kohonen feature maps) and like them it can be seen as a special kind of artificial neural network.
		Visualizing learning vector quantization is obviously very simple, as long as we confine ourselves to two input attributes plus an optional class attribute.
		However, learning vector quantization may also be applied when no class information is available, by simply treating all training patterns as belonging to the same class.
	fuzzy.cs.uni-magdeburg.de /~borgelt/doc/lvqd (1938 words)

	Learning Vector Quantization (LVQ)
		Vector quantization is one example of competitive learning.
		In vector quantization, we assume there is a codebook which is defined by a set of M prototype vectors.
		Vector quantization can be used for (lossy) data compression.
	www.willamette.edu /~gorr/classes/cs449/Unsupervised/competitive.html (418 words)

	Clustering and Vector Quantization
		In general, quantization is the process of converting a continuous-valued signal into a discrete one.
		In the vector case, we can imagine histogramming the data and using the indexes of the histogram cells to represent the data.
		If we want to get the best approximation for our data, we need to quantize it as finely as possible, which means generating as many prototypes as possible.
	pages.sbcglobal.net /pstetson/PR/ClustVQ.html (1771 words)

	Method and system for multi-rate lattice vector quantization of a signal patent invention (Site not responding. Last check: 2007-10-03)
		The transform coefficients are quantized with a quantizer Q to obtain an index or a set of indices for characterizing the quantized transform coefficients of the frame.
		The entry selected to quantize the input vector is typically the nearest neighbor in the codebook according to a distance criterion.
		To reduce the quantization error, the residual error e.sub.1=x-y.sub.1 of the first stage, which is the difference between the input vector x and the selected first-stage codevector y.sub.1, is then quantized with a second-stage codebook C.sub.2 into a codevector y2.
	www.freshpatents.com /Method-and-system-for-multi-rate-lattice-vector-quantization-of-a-signal-dt20051229ptan20050285764.php (1687 words)

	Situs Program Package
		Vector quantization offers a flexible way to develop a reduced representation of 3D biological data from a variety of biophysical sources.
		Given a certain number of vectors, we would like to make sure that the vector positions are statistically reliable and reproducible for them to be useful for the registration of the encoded features.
		In signal processing this error measures the fidelity of the data encoding (the original use of vector quantization was in data compression).
	situs.biomachina.org /fvq.html (657 words)

	Vector quantization - Wikipedia, the free encyclopedia
		The lower-space vector requires less storage space and the data is thus compressed.
		The transformation into the subspace is usually achieved through projection, or by using a codebook.
		Twin vector quantization (VQF) is part of the MPEG-4 standard dealing with time domain weighted interleaved vector quantization.
	en.wikipedia.org /wiki/Vector_quantization (196 words)

	Vector quantization method and apparatus (US5194864)
		In a vector quantization method and apparatus of the invention, in the network learning step, learning is performed such that output units corresponding to the indexes of a plurality of code book vectors Yi are caused to ignite more as the output units are located nearer to an input vector.
		The search order is determined such that the code book vectors are searched in the order of the indexes corresponding to the decreasing magnitudes of ignition from the output units.
		In the strain calculation step, the strains between the code book vectors Yi selected in the determined order and the input vector X are calculated in units of dimensions.
	www.delphion.com /details?pn10=US05194864 (331 words)

	PCA Colour Image Compression Using Vector Quantization
		The initial 256 code vectors in the codebook are selected from the 3-dimensional cluster diagram of first three components.
		As the vector quantization is quite efficacious method for data compression, then it is selected to compress the PCA colour image.
		The initial code vector in the vector quantization's codebook are defined by the most frequently appear colours in the PCA image in order to reduce the computation time and also the mean square error.
	www.gisdevelopment.net /aars/acrs/1999/ps5/ps5072pf.htm (985 words)

	Nearly Optimal Vector Quantization via Linear Programming (Site not responding. Last check: 2007-10-03)
		We present new vector quantization algorithms based on the theory developed by the authors.
		The new approach is to formulate a vector quantization problem as a 0-1 integer linear program.
		These methods lead to the first known polynomial-time full-search vector quantization codebook design algorithm and tree pruning algorithm with provable worst-case performance guarantees.
	www.cs.duke.edu /~jsv/Papers/catalog/node84.html (130 words)

	[76] What is Vector Quantization? (Site not responding. Last check: 2007-10-03)
		Some vector quantization software for data analysis that is available in the ftp://cochlea.hut.fi/pub/ directory.
		A VQ-based codec that is based on the Predictive Residual Vector Quantization is in ftp://mozart.eng.buffalo.edu/pub/prvq_codec/ VQ software is also available in ftp://isdl.ee.washington.edu/pub/VQ/ For a book on Vector Quantization, see the reference (Gersho and Gray) given in item 7 of this FAQ.
		A short introduction to Vector Quantization, written by Alex Zatsman : In Scalar Quantization one represents the values by fixed subset of representative values.
	www.faqs.org /faqs/compression-faq/part2/section-7.html (404 words)

	Light Field Compression using Wavelet Transform and Vector Quantization
		This project suggests that by using wavelet transform and vector quantization we can achieve pretty high compression ratio with minimun degradation in rendering quality.
		Wavelet transform followed by vector quantization is very useful for compressing light fields.
		Vector quantization is particulary suitable for real-time decoding since only table look-ups are involved.
	graphics.stanford.edu /~liyiwei/project/ee372/report.html (1784 words)

	Compression
		VQ is a generalization of scalar quantization: It quantizes vectors (contiguous blocks) of pixels rather than individual pixels
		VQ can also be used as the quantization stage of a general lossy compression scheme, especially where the transform stage does not decorrelate completely, such as in certain applications of wavelet transforms
		For global codebooks, the training set is the set of all the blocks of a representative subset of images selected from the class of images of the application.
	www.seas.gwu.edu /~ayoussef/cs225/vectorquantization.html (702 words)

	[No title]
		The Learned Vector Quantization algorithm (LVQ) was invented by Tuevo Kohonen (Fausett, 1994; Kohonen, 1990), who also invented the Self-Organizing Feature Map.
		In essence, the algorithm attempts to move the exemplar vectors to positions that reflect the centers of clusters in the training data.
		It is multiplied by the vector of probability estimates, resulting in a vector of cost estimates, and the case is assigned to the class with the lowest cost estimate.
	www.statsoft.com /textbook/glosl.html (4798 words)

	DataCompression.info - Quantization (Site not responding. Last check: 2007-10-03)
		Quantization techniques generally compress by compressing a range of values to a single quantum value.
		Vector Quantization and Signal Compression by Allen Gersho
		In this article Sjaak uses octree color quantization to reduce a JPEG image with millions of colors down to a GIF with 256, as well as giving you a look or two at Microsoft's nice GDI+ API.
	datacompression.info /Quantization.shtml (978 words)

	A.1 Tree Structured Vector Quantization (TSVQ) (Site not responding. Last check: 2007-10-03)
		We propose to apply the Tree Structured Vector Quantizer (TSVQ) [.Baras Wolk 1993.] to the multiscale representation of spectra of selected databases.
		The algorithm is first applied to the coarsest resolution of the data vectors, which results in relatively few clusters or classes.
		For instance, vector quantization can be viewed both as a compression and as a classification algorithm (as we have been emphasizing), and hence one can measure its performance by satisfying multi-objective functions, e.g., incorporating the Bayes risk (measuring classification performance) in the distortion measure (measuring compression performance).
	www.isr.umd.edu /CAAR/proposal/2/node3.html (409 words)

	The LBG-U method for vector quantization - an improvement over LBG inspired from neural networks - Fritzke ... (Site not responding. Last check: 2007-10-03)
		Abstract: A new vector quantization method -- denoted LBG-U -- is presented which is closely related to a particular class of neural network models (growing self-organizing networks).
		Each time LBG has converged, however, a novel measure of utility is assigned to each codebook vector.
		Thereafter, the vector with minimum utility is moved to a new location, LBG is run on the resulting modified codebook until convergence, another vector...
	citeseer.ist.psu.edu /fritzke97lbgu.html (464 words)

	CREATIVE: vector quantization
		These objects are semiregular triangular surfaces remeshed by the technique Multiresolution Adaptive Parameterization of Surfaces (MAPS) ot the technique Normal mesh from the irregular ones.
		In order to accomplish our intent, we use a loss compression scheme based on transformation, quantization and codification steps.
		We introduce the use of Zn lattice vector quantizer (LVQ) with a prefix (product) code used in a novel way to encode the wavelet coefficients generated in the transformation step.
	wwwi3s.unice.fr /~creative/html/vector-quantization.html (144 words)

	Parallel Vector Quantization (Site not responding. Last check: 2007-10-03)
		A Vector Quantizer can be defined as a mapping of input vectors of a particular size into a finite set of codevectors in a codebook.
		Signal compression is achieved in VQ by using a codebook with relatively few codevectors compared to the number of possible signal vectors.
		The decoder is written in ANSI C. The programs are written to accomodate a range of codebook and vector sizes.
	www.elen.utah.edu /~mathews/pvq.html (172 words)

	Variable-Precision Arithmetic for Vector Quantization (ResearchIndex) (Site not responding. Last check: 2007-10-03)
		Abstract: of the Dissertation Variable-Precision Arithmetic For Vector Quantization by Raffi Dionysian Doctor of Philosophy in Computer Science University of California, Los Angeles, 1994 Professor Milos D. Ercegovac, Chair This research proposes and investigates a method for the storage and computation in Vector Quantization (VQ) -- a promising technique for image/speech compression.
		83 Vector quantization in speech coding (context) - Makhoul, Roucos et al.
		2 Vector quantizer architectures for speech and image coding (context) - Abut, Tao et al.
	citeseer.ist.psu.edu /498432.html (752 words)

	Keith Price Bibliography Vector Quantization, VQ
		Yamaguchi, H. Vector Quantization of Differential Luminance and Chrominance Signals,
		A vector quantization scheme using prequantizers of human visual effects,
		Cziho, A. Lovanyi, I. Solaiman, B. Cazuguel, G., and Roux, C. Derailment-Free Finite State Vector Quantization Using Conditional Histogram.
	iris.usc.edu /Vision-Notes/bibliography/image-proc147.html (1203 words)

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