LBGS: a smart approach for very large data sets vector quantization

Campobello, Giuseppe; Mantineo, Mirko; Patanè, Salvatore; Russo, Marco

doi:10.1016/j.image.2004.10.001

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…Techniques for fast and efficient codebook generation of vector quantization have been reported in the literature. Various improvements [5,8,11] for LBG based codebook construction algorithm have been adopted to minimize the computing time and to generate better codebook for representing the input vector. Most of the existing vector quantization algorithms are experimented with monochrome images and few vector quantization techniques have been reported with color image coding.…”

Section: Introductionmentioning

confidence: 99%

Fast vector Quantization of Color Image Coding with Single Codebook based on Orthogonal Polynomials

Krisshnamoorthy¹,

Punidha²

2012

IJCA

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In this paper, a new fast vector quantization encoding technique for transform coding of RGB color images that does not require a color coordinate conversion matrix is proposed. The proposed work directly applies the orthogonal polynomials transformation on the input image and transformed training set with reduced dimension is obtained for the vector quantization and hence the proposed work has reduced computational complexity. In the codebook generation phase of vector quantization encoding, a new transformed binary tree algorithm is proposed to construct a single codebook for all the three color components, utilizing the inter-correlation property of the individual color plane as well as interactions among the color planes with the proposed transformation and so a big saving in codebook construction time is achieved. A new transformed tree structured codeword matching algorithm is proposed in order to further reduce the vector quantization encoding time for finding the closest codeword of an input vector. The experimental results show that the proposed algorithm greatly reduces the encoding time when compared with recent fast vector quantization algorithms.

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Section: Introductionmentioning

confidence: 99%

Fast vector Quantization of Color Image Coding with Single Codebook based on Orthogonal Polynomials

Krisshnamoorthy¹,

Punidha²

2012

IJCA

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“…There is, however, a growing interest in the design and analysis of distributed systems, multi-agent systems, and distributed modeling (Acampora and Loia, 2008;Bouchon-Meunier, 1998;Ferrero and Salicone, 2007;Genesereth and Ketchpel, 1994;Pedrycz and Vukovich, 2002). This interest is supported by a wealth of pertinent methodologies and algorithmic developments, (Ayad and Kamel, 2003;Bickel and Scheffer, 2004;Campobello et al, 2005;Costa da Silva and Klusch, 2006;Gersho and Gray, 1992;Merugu and Ghosh, 2005;Pedrycz, 2002;Pedrycz and Rai, 2008;Skillicorn and McConnell, 2008;Stubberud and Kramer, 2006;Tsoumakas et al 2004;Wiswedel and Berthold, 2007).…”

Section: Introduction and Motivating Insightsmentioning

confidence: 99%

The development of granular metastructures and their use in a multifaceted representation of data and models

Pedrycz¹

2010

Kybernetes

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Purpose -The purpose of this paper is to show that exploiting fundamental ideas of granular computing can lead to further conceptual developments of granular metastructures, which are inherently associated with computing involving a large number of individual datasets; and to show that such processing leads to the representatives of information granules and granular models in the form of metastructures and metamodels. Design/methodology/approach -The formulation of the concept of granular metastructures is provided and presented along with some essential algorithmic developments and associated optimization strategies. The overall methodological framework is the one of granular computing, especially fuzzy sets and fuzzy sets of higher type. Given the structural facet of optimization, the paper stresses the relevance of the use of evolutionary optimization.Findings -This paper focused on the underlying concepts and while it elaborated on some development aspects and optimization tools, it should be stressed that further refinement and a thorough exploitation of optimization techniques in application to the inherently combinatorial facet of the problem are to be pursued in detail. Practical implications -The introduced approach and algorithms could be of interest when solving problems of granular metastructures, in particular those encountered in knowledge-based systems. Originality/value -The main aspects of originality concern a formulation of the concept of granular metastructures and their design, based on granular evidence (experimental data) of lower type. A constructive way of forming type-2 fuzzy sets via the principle of justifiable granularity exhibits a significant level of originality and offers a general way of designing information granules.

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“…In image processing and other applications, VQ has been used to model pixel neighborhoods as the closest code vectors that play the role of conditional context in a Bayesian framework for image filtering [19], design compression codes for image compression [20], quantize wavelet coefficients for video image transmission [21], and most recently to code biological features for pattern classification [17,18,22]. The development of new VQ-based methodology is still an active area of research in signal, image, and speech processing as well as other pattern recognition problems [9,[23][24][25][26][27][28][29][30]. Among different VQ techniques, the LBG (Linde, Buzo, Gray) algorithm [1] is a wellknown VQ method that has been widely used in many applications as well as modified for improvement.…”

Section: Introductionmentioning

confidence: 99%