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lattice vector quantization of generalized Gaussian sources

IEEE TRANSACTIONS ON INFORMATION THEORY 1997年第1期43卷 92-103页

作者： Chen, F Gao, Z Villasenor, J Electrical Engineering Department University of California Los Angeles Los Angeles CA USA

Many important sources of data including subband image and speech coefficients are highly peaked and can be well-modeled by the family of generalized Gaussian (GG) PDF's parameterized by a shape parameter nu. We present here algorithms for defining and quantizing to a Z lattice in which the boundary is optimized to the characteristics of GG sources, and show that these techniques lead to high performance and low complexity for bit rates and dimensions that are of interest in a number of practical coding applications. We derive an analytical description of the granular and overload distortion valid for low and moderate bit rates, and also provide a description of quantizer performance in the limit of high rate and dimension.

关键词： lattice vector quantization memoryless generalized Gaussian sources

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lattice vector quantization with reduced or without look-up table.

Lattice vector quantization with reduced or without look-up ...

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Visual Communications and Image Processing 1998 Conference

作者： Rault, P Guillemot, C CCETT F-35510 Cesson Sevigne France

ISBN: (纸本)0819427497

This paper describes a new vector indexing algorithm for lattice vector quantization, (LVQ). The technique applies to a large class of lattices such as Z(n), A(n), D-n or E-8, widely used in signal compression. Relying on a partitioning of the events sources, based on a notion of leaders as proposed in,(1) it allows to trade vector look-up table size for arithmetic operations. At the cost of a very small number of integer arithmetic operations, the algorithm leads to a very significant reduction of the vector look-up tables. This in turn leads to reduced encoder and decoder complexities. The introduction of the concept elf 'absolute' leaders, and of the corresponding coding and decoding algorithms, provides additional flexibility in trading table size for arithmetic operations. The association of these vector indexing techniques with product codes, in the framework of lattice vector quantization, leads to increased compression performances.

关键词： lattice vector quantization vector indexing entropy coding data compression image and video coding

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lattice vector quantization with Direct Indexing as Data Differencing Technique 9

Lattice Vector Quantization with Direct Indexing as Data Dif...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Khandelwal, R. R. Purohit, P. K. Shriwastava, S. K. Shri Ramdeobaba Coll Engn & Management Nagpur Maharashtra India Natl Inst Tech Teachers Training & Res Bhopal India Shri Balaji Inst Technol & Management Betul India

ISBN: (纸本)9781479964994

A huge range of approaches to deal with the task of image coding with lossy compression based on wavelet and vector quantization has been developed. lattice vector quantization (LVQ), a variant of vector quantization is a lossy image compression technique. In this paper LVQ is applied on the transformed image as lossy and data differencing technique. While used as data differencing technique, the indices of quantized vectors with the indices of error vectors are sent to the decoder. To achieve this, extra computations are required at the encoder. Indices for lattice vectors are generated by proposed direct indexing method which is independent of the indexing of leaders and enumeration of the lattice points. PSNR values and visual performance of various test images are compared in both cases.

关键词： lattice vector quantization compression data differencing indexing

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Secure Medical Image Communication Using Fragile Data Hiding Based on Discrete Wavelet Transform and A5 lattice vector quantization

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IEEE ACCESS 2023年 11卷 9701-9715页

作者： Akhtarkavan, Ehsan Majidi, Babak Mandegari, Amirsina Khatam Univ Fac Engn Dept Comp Engn Tehran *** Iran Pasargad Inst Adv Innovat Solut Tehran Iran

Secure communication of medical images is essential to telemedicine. Message Authentication Codes (MAC) can be embedded inside medical images to protect their integrity. Fragile watermarking algorithms are suitable options since they can be used to detect any tampering attempt. In this paper, a novel fragile data-hiding algorithm based on Integer-to-Integer Discrete Wavelet Transforms (IIDWT) and A(5) lattice vector quantization (LVQ) is proposed. In the proposed data-hiding algorithm, a combination of the medical image Metadata and a MAC is embedded into the medical image. The Metadata includes information about the patient such as name, family, birthday, the place where it is created such as the name of the hospital, and the physician's name. To preserve the privacy of the patients and the physician/hospital, the Metadata is then replaced with fake information. The receiver can extract the Metadata and the MAC. If the extracted MAC is the same as the expected MAC, the integrity of the medical image is guaranteed. Otherwise, a tampering attempt is detected. The proposed algorithm can embed 50% more data than similar algorithms in medical images while keeping the Peak Signal to Noise Ratio (PSNR) in acceptable ranges. Furthermore, the proposed algorithm is applied to a dataset of medical images and high PSNR values above 53.88 dB are experienced.

关键词： Data hiding privacy medical image privacy fragile data hiding integer discrete wavelet transforms lattice vector quantization

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Index assignment for 3-description lattice vector quantization based on A₂ lattice

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SIGNAL PROCESSING 2008年第11期88卷 2754-2763页

作者： Liu, Minglei Zhu, Ce Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Multiple description lattice vector quantization (MDLVQ) is an effective technique to realize multiple description coding. In this paper, we investigate a 3-description lattice vector quantization (LVQ) design using A(2) lattice, which finds a 3-tuple combination of sublattice points to represent each fine lattice point, based on two principles that: each edge of the triangle formed by the 3-tuple points needs to be as short as possible, and the gravity center of the triangle is as close as possible to the fine lattice point. Following a delicate sublattice partition, a well-designed construction and mapping of each fine lattice point to a 3-tuple of sublattice points is developed to minimize the side distortion. The proposed index assignment is shown to achieve smaller distortion (up to 0.58 dB for Gaussian source) than some other 3-description index assignments, the 3-description LVQ achieves better expected Compared with a 2-description LVQ p rate-distortion performance in most cases of high description loss rates, while it also exhibits more graceful coding results with significantly smaller side distortions. Numerical analysis on Gaussian source and the simulations on image source validate the effectiveness of our proposed index assignment scheme. (C) 2008 Elsevier B.V. All rights reserved.

关键词： index assignment lattice vector quantization multiple description coding (MDC) sublattice

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Optimized multiple description lattice vector quantization for wavelet image coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2007年第7期17卷 912-917页

作者： Bai, Huihui Zhu, Ce Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

Multiple description (MD) coding is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. In this paper, an effective MD image coding scheme is introduced based on the MD lattice vector quantization (MDLVQ) for the wavelet transformed images. In view of the characteristics of wavelet coefficients in different frequency subbands, MDLVQ is applied in an optimized way, including an appropriate construction of wavelet coefficient vectors, the optimization of MDLVQ encoding parameters such as the choice of sublattice index values and the quantization accuracy for different subbands. More importantly, optimized side decoding is employed to predict lost information based on inter-vector correlation and an alternative transmission way for further reducing side distortion. Experimental results validate the effectiveness of the proposed scheme with better performance than some other tested MD image codecs including that based on optimized MD scalar quantization.

关键词： image coding lattice vector quantization multiple description (MD) coding wavelet transform

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M-Description lattice vector quantization: Index Assignment and Analysis

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第6期57卷 2258-2274页

作者： Liu, Minglei Zhu, Ce Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, we investigate the design of symmetric entropy-constrained multiple description lattice vector quantization (MDLVQ), more specifically, MDLVQ index assignment. We consider a fine lattice containing clean similar sublattices with S-similarity. Due to the S-similarity of the sublattices, an M-fraction lattice can be used to regularly partition the fine lattice with smaller Voronoi cells than a sublattice does. With the partition, the MDLVQ index assignment design can be translated into a transportation problem in operations research. Both greedy and general algorithms are developed to pursue optimality of the index assignment. Under high-resolution assumption, we compare the proposed schemes with other relevant techniques in terms of optimality and complexity. Following our index assignment design, we also obtain an asymptotical close-form expression of k-description side distortion. Simulation results on coding different sources of Gaussian, speech and image are presented to validate the effectiveness of the proposed schemes.

关键词： Index assignment lattice lattice vector quantization multiple description coding sublattice transportation problem

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Successive refinement lattice vector quantization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2002年第12期11卷 1337-1348页

作者： Mukherjee, D Mitra, SK Univ Calif Santa Barbara Image Proc Lab Dept Elect & Comp Engn Santa Barbara CA 93106 USA

lattice vector quantization (LVQ) solves the complexity problem of LBG based vector quantizers, yielding very general codebooks. However, a single stage LVQ, when applied to high resolution quantization of a vector, may result in very large and unwieldy indices, making it unsuitable for applications requiring successive refinement. The goal of this work is to develop a unified framework for progressive uniform quantization of vectors without having to sacrifice the mean- squared-error advantage of lattice quantization. A successive refinement uniform vector quantization methodology is developed, where the codebooks in successive stages are all lattice codebooks, each in the shape of the Voronoi regions of the lattice at the previous stage. Such Voronoi shaped geometric lattice codebooks are named Voronoi lattice VQs (VLVQ). Measures of efficiency of successive refinement are developed based on the entropy of the indices transmitted by the VLVQs. Additionally, a constructive method for asymptotically optimal uniform quantization is developed using tree-structured subset VLVQs in conjunction with entropy coding. The methodology developed here essentially yields the optimal vector counterpart of scalar "bitplane-wise" refinement. Unfortunately it is not as trivial to implement as in the scalar case. Furthermore, the benefits of asymptotic optimality in tree-structured subset VLVQs remain elusive in practical nonasymptotic situations. Nevertheless, because scalar bitplane- wise refinement is extensively used in modern wavelet image coders, we have applied the VLVQ techniques to successively refine vectors of wavelet coefficients in the vector set-partitioning (VSPIHT) framework. The results are compared against SPHIT and the previous Successive Approximation Wavelet vector quantization (SA-W-VQ) results of Sampson, da Silva, and Ghanbari.

关键词： entropy coding lattice vector quantization vector SPIHT Voronoi region wavelets zerotree

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Low-complexity source coding using Gaussian mixture models, lattice vector quantization, and recursive coding with application to speech spectrum quantization

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2006年第2期14卷 524-532页

作者： Subramaniam, AD Gardner, WR Rao, BD Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92122 USA

In this paper, we use the Gaussian mixture model (GMM) based multidimensional companding quantization framework to develop two important quantization schemes. In the first scheme, the scalar quantization in the companding framework is replaced by more efficient lattice vector quantization. Low-complexity lattice pruning and quantization schemes are provided for the E-8 Gossett lattice. At moderate to high bit rates, the proposed scheme recovers much of the space-filling loss due to the product vector quantizers (PVQ) employed in earlier work, and thereby, provides improved performance with a marginal increase in complexity. In the second scheme, we generalize the compression framework to accommodate recursive coding. In this approach, the joint probability density function (PDF) of the parameter vectors of successive source frames is modeled using a GMM. The conditional density of the parameter vector of the current source frame based on the quantized values of the parameter vector of the previous source frames is used to generate a new codebook for every current source frame. We demonstrate the efficacy of the proposed schemes in the application of speech spectrum quantization. The proposed scheme is shown to provide superior performance with moderate increase in complexity when compared with conventional one-step linear prediction based compression schemes for both narrow-band and wide-band speech.

关键词： Gaussian mixture models lattice vector quantization memoryless coders multidimensional companding recursive coding speech spectrum quantization vector quantization wideband speech

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Fast lexicographical order-based encoderfor lattice vector quantization of Generalized Gaussian sources using pre-computed n-balls cardinalities

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2018年 62卷 42-50页

作者： Guillemot, Ludovic Moureaux, Jean-Marie Yooz ITESOFT Yooz Grp Parc Andron Sequoia F-30470 Aimargues France Nancy Univ CNRS CRAN UMR 7039 F-54506 Vandoeuvre Les Nancy France

A fast indexing method dedicated to lattice vector quantization (LVQ), called stack indexing (SI), is presented. It is based on the approach previously proposed by Loyer et al. (2003). Our method addresses the problem of enumerating codebook vectors in lattice Z(n) and is designed for the compression of Generalized Gaussian sources. Indexing is the key point of the lossless stage of LVQ. It usually requires a high number of additions and thus is often critical in terms of execution time. To circumvent this drawback, SI uses pre-computed cardinalities of n-balls in Z(n) to enumerate lattice vectors in the lexicographical order. The algorithmic complexity of the SI is low and the number of additions performed does not depend on the dynamic range of the source which makes SI well suited for high dynamic sources. Experimental results performed in still image coding using a discrete wavelet transform confirm that the computational cost is significantly decreased compared to the previous approach.

关键词： Source coding Image compression lattice vector quantization Indexing Generalized Gaussian sources

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