4 edition of Vector quantization for efficient coding of upper subbands found in the catalog.
Vector quantization for efficient coding of upper subbands
by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va
Written in English
|Statement||V.J. Zeng, Y.F. Huang.|
|Series||[NASA contractor report] -- NASA CR-196804., NASA contractor report -- NASA CR-196804.|
|Contributions||Huang, Y. F., United States. National Aeronautics and Space Administration.|
|The Physical Object|
The authors show that full-search entropy-constrained vector quantization of image subbands results in the best performance, but is computationally expensive. Lattice quantizers yield a coding efficiency almost indistinguishable from optimum full-search entropy-constrained vector quantization. AN EFFICIENT VECTOR QUANTIZATION METHOD FOR IMAGE COMPRESSION WITH CODEBOOK GENERATION USING MODIFIED K-MEANS INTRODUCTION Image compression is a technique of competently coding digital image, to lessen the number of bits required in representing image. The main aim of image compression is to decrease the storage space.
The vector quantization is a classical quantization technique for signal processing and image compression which allows the modelling of probability density functions by the distribution of prototype vectors. Main use of vector quantization (VQ) is for data compression [2 and 3]. Vector Quantization in Speech Coding Invited Paper Quantization, the process of approximating continuous-ampli- tude signals by digital (discreteamplitude) signals, is an important aspect of data compression or coding, the field concerned with the reduction of .
VQ is an efficient information source coding method. The principle of it is constructing a vector based on several scalar data group, so the vector quantization coding is superior to scalar quantization coding. LBG algorithm was named after its proposers Linde et al. and afterwards there were many other improved methods. This method can. Su et al. developed a hybrid coding system by using SPIHT and VQ for image compres-sion in . Abdel-Galil et al. applied VQ to power systems for classifying power quality disturbances . When the code vector and code book sizes become large enough, the distortion of the vector quantizer approaches the lower bound of the distortion-rate rela-.
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Vector quantization is a lossy compression technique used in speech and image coding. In scalar quantization, a scalar value is selected from a finite list of possible values to represent a sample.
In vector quantization, a vector is selected from a finite list of possible vectors to represent an input vector of samples. The key operation in a. Get this from a library. Vector quantization for efficient coding of upper subbands. [V J Zeng; Y F Huang; United States. National Aeronautics and Space Administration.].
Vector Quantization of Image Subbands: A Survey which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system.
Vector quantization for efficient coding of upper subbands. By Y. Huang and W. Zeng. Abstract. This paper examines the application of vector quantization (VQ) to exploit both intra-band and inter-band redundancy in subband coding.
The focus here is on the exploitation of inter-band dependency. It is shown that VQ is particularly suitable Author: Y. Huang and W. Zeng. A novel vector subbands/ECVQ approach for image coding is presented which incorporates the concept of activity map.
A key issue in vector subband coding is the efficient compression of. Subband coding of images using vector quantization Abstract: A novel two-dimensional subband coding technique is presented that can be applied to images as well as speech. A frequency-band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 equal-rate subbands.
Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system.
Quantization noise reduction with FMRA subband systems. Since one of the fundamental purposes of subband processing is to achieve greater data compression, quantization at subbands is a key element in subband coding systems.
Therefore, the effect of noise introduced by quantization, and its consequences at the output of the system, is an. Page - Juang and AH Gray, Jr. Multiple stage vector quantization for speech coding. Appears in 27 books from Page - RM Gray. Buzo. AH Gray, Jr., and Y. Matsuyama, "Distortion measures for speech processing, .
Image authentication, which is provided with capabilities of tamper detection and data recovery, is an efficient way to protect the contents of digital images.
Vector quantization (VQ) is a data compression method. A VQ-compressed code is not only a significant image authentication feature but also applicable in restoring possibly damaged pixels.
Vector quantization, also called "block quantization" or "pattern matching quantization" is often used in lossy data compression. It works by encoding values from a multidimensional vector space into a finite set of values from a discrete subspace of lower dimension.
A lower-space vector requires less storage space, so the data is compressed. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Subband and wavelet decompositions are powerful tools in image coding, because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting which allows for efficient coding matched to the statistics of each.
The distance of the vector to be quantized to all the vectors in the codebook is found. Using for example where you create distance metric and the one vector in the code book that is closest to the vector to be quantized is used as the reconstruction value of the vector.
Each vector in the code book has an index. Vector Quantization for Efficient Coding of Upper Subbands 1 W.J. Zeng Department of Electrical Engineering Princeton University Princeton, N.J. e-maih Y.F. Huang Lab. Image & Signal Analysis Department of Electrical Engineering University of Notre Dame Notre Dame, 'IN Abstract.
Subband coding and vector quantization have been shown to be effective methods for coding images at low bit rates. In this paper, we propose a new subband finite-state vector quantization scheme that combines the SBC and FSVQ. A frequency band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 subbands.
EPA2 EPA EPA EPA2 EP A2 EP A2 EP A2 EP A EP A EP A EP A EP A EP. Vector quantization of image subbands: a survey. By Pamela C. Cosman, Robert M. Gray and Martin Vetterli.
Abstract. Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their.
The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g.
shapes, colour) of the pictures but on the picture name. Due to the delaying property of the typical image spectrum from low frequency to high frequency, the decomposition produces subbands with flatter spectrum.
Encoding these subbands with greatly reduced statistical correlation improves the coding efficiency according to the rate distortion theory .
quantization algorithm. The codebooks are predetermined on a set of images to speed up transmission. The outline of the method is presented, as well as some experimental results.
uction Vector quantization (VQ) is a powerful lossy compression scheme for image coding, but it exhibits two drawbacks. First, when applied. The coding technique provides for excellent speech coding and reproduction at rates as low as kb/s In terms of signal to noise ratio (SNR), the algorithm with the above specific implementation, outperforms the prior Mazor et al.
algorithm by over 2 dB at 16 kb/s and by more than 1 dB at kb/s.An essential book for researchers and engineers working on vector quantization.
It provides a (nearly) exhaustive description of the available VQ methods, with analyses and discussions. The book concentrates on VQ, so don't buy it if you're not interested in that only.
Most math tools s: 3.Compression in general is intended to provide efficient representations of data while preserving the essential information contained in the data. This book is devoted to the theory and practice of signal compression, i.
e., data compression applied to signals such as speech, audio, images, and video signals (excluding other data types such as.