Vector quantization codebook generation using simulated annealing

Flanagan, J. Kelly; Morrell, Darryl; Frost, R.L.; Read, Christopher; Nelson, Brent

doi:10.1109/icassp.1989.266790

Cited by 35 publications

(6 citation statements)

References 7 publications

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“…It is the standard choice of a typical body (see Section I-B) in the computation of lattice parameters. The volume of Ω § 6 The normalized second moment (11) is…”

Section: Latticesmentioning

confidence: 99%

“…Among the large number of training algorithms that have been proposed, we mention [3], 1 [4], [5, chs. 5 and 7], and [6].…”

Section: Introduction: Vector Quantization and Latticesmentioning

confidence: 99%

“…We use "lattice point" and "lattice vector" interchangeably 6. The volume is more commonly given in the form (det( )) BB T 1 2 , which allows for nonsquare generator matrices.…”

mentioning

confidence: 99%

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Optimization of lattices for quantization

Agrell

Eriksson

1998

IEEE Trans. Inform. Theory

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A training algorithm for the design of lattices for vector quantization is presented. The algorithm uses a steepest descent method to adjust a generator matrix, in the search for a lattice whose Voronoi regions have minimal normalized second moment. The numerical elements of the found generator matrices are interpreted and translated into exact values. Experiments show that the algorithm is stable, in the sense that several independent runs reach equivalent lattices. The obtained lattices reach as low second moments as the best previously reported lattices, or even lower. Specifically, we report lattices in 9 and 10 dimensions with normalized second moments of 0.0716 and 0.0708, respectively, and nonlattice tessellations in 7 and 9 dimensions with 0.0727 and 0.0711, which improves on previously known values. The new 9and 10-dimensional lattices suggest that Conway and Sloane's conjecture on the duality between the optimal lattices for packing and quantization might be false. A discussion of the application of lattices in vector quantizer design for various sources, uniform and nonuniform, is included.

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“…It is the standard choice of a typical body (see Section I-B) in the computation of lattice parameters. The volume of Ω § 6 The normalized second moment (11) is…”

Section: Latticesmentioning

confidence: 99%

“…Among the large number of training algorithms that have been proposed, we mention [3], 1 [4], [5, chs. 5 and 7], and [6].…”

Section: Introduction: Vector Quantization and Latticesmentioning

confidence: 99%

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Optimization of lattices for quantization

Agrell

Eriksson

1998

IEEE Trans. Inform. Theory

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“…Simulated annealing ͑SA͒ was found to be an effective stochastic optimization technique for VQ design. 2,3 However, one must be careful with the annealing schedule, the rate at which the temperature is lowered. The schedule for achieving global optimum was shown in Ref.…”

Section: Introductionmentioning

confidence: 99%

Genetic entropy-constrained vector quantizer design algorithm

Hwang

Hong

1999

Opt. Eng

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A novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm is presented. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches a nearby global optimum rather than a local one. Simulation results show that, when applied to image coding, the technique achieves higher peak SNR (PSNR) and image quality than techniques using the ECVQ algorithm.

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“…Based on the codebook generation method, the VQ is classified into two types: (i) universal VQ and (ii) adaptive vector quantisation (AVQ). The universal VQ method generates the static codebook using any one of the training algorithm such as Linde -Buzo -Gary [16,17], pair-wise nearest neighbour [18], simulated annealing [19], genetic algorithm [20,21] and self-organising feature map [22,23] from the larger training set of images and is fixed at both encoder and decoder. Therefore the advantage of universal VQ methods is that there is no overhead required for transmitting the codebook.…”

Section: Introductionmentioning

confidence: 99%

An orthogonal polynomials transform-based variable block size adaptive vector quantisation for colour image coding

Krishnamoorthy

Punidha

2012

IET Image Process.

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In this paper, a new adaptive vector quantisation with variable block size based on orthogonal polynomials is proposed for colour image coding. The input colour image is partitioned into fixed larger block sizes and the homogeneity/complexity of the local region is tested with the mean-normalised variance classifier. The homogeneous regions are encoded with fixed larger blocks and the complex regions are further divided into smaller block sizes for encoding as it gives the wealth of low-level information such as the high detail and high number of contrast edges. The integer-based orthogonal polynomials transform is then applied on the variable block sizes utilising the inter-correlation property of individual colour planes and interactions among the colour planes so as to reduce the vector dimension and hence the less computational cost. The proposed technique generates two dynamic codebooks and two backup codebooks adaptively and use rate-distortion cost criteria to encode both the homogeneous and complex regions separately. Since the proposed method encodes all the three colour components in single pass, the colour image encoding time is slightly higher than that of grey scale image coding but in contrast to three times in the existing colour image coding techniques.

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Vector quantization codebook generation using simulated annealing

Cited by 35 publications

References 7 publications

Optimization of lattices for quantization

Optimization of lattices for quantization

Genetic entropy-constrained vector quantizer design algorithm

An orthogonal polynomials transform-based variable block size adaptive vector quantisation for colour image coding

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