Efficient vector quantization using genetic algorithm

Sun, Hongwei; Lam, Kwok‐Yan; Chung, Siu-Leung; Dong, Weiming; Gao, Ming; Sun, Jun

doi:10.1007/s00521-004-0455-7

Cited by 26 publications

(19 citation statements)

References 21 publications

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“…Research interests in heuristic search algorithms with underpinnings in natural and physical processes began in the 1970s, when Holland [10] proposed GA. GA generates a sequence of populations using a selection mechanism, and applies crossover and mutation as search mechanisms. It has demonstrated considerable success in providing good solutions to many complex optimization problems [18,26], such as capital budgeting, vehicle routing problem, critical path problem, parallel machine scheduling, redundancy optimization, open inventory network etc. The advantage of GA is due to its ability to obtain a global optimal solution fairly in a multidimensional search landscape, which has several locally optimal solutions as well.…”

Section: Related Workmentioning

confidence: 99%

Optimized tile-based texture synthesis

Dong

Zhou

Paul

2007

Proceedings of Graphics Interface 2007 on - GI '07

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One significant problem in tile-based texture synthesis is the presence of conspicuous seams in the tiles. The reason is that the sample patches employed as primary patterns of the tile set may not be well stitched if carelessly picked. In this paper, we introduce an optimized approach that can stably generate an ω-tile set of high pattern diversity and high quality. Firstly, an extendable rule is introduced to increase the number of sample patches to vary the patterns in an ω-tile set. Secondly, in contrast to the other concurrent techniques that randomly choose sample patches for tile construction, our technique uses Genetic Algorithm to select the feasible patches from the input example. This operation insures the quality of the whole tile set. Experimental results verify the high quality and efficiency of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

Optimized tile-based texture synthesis

Dong

Zhou

Paul

2007

Proceedings of Graphics Interface 2007 on - GI '07

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“…Moreover, there have been some efforts in using evolutionary algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO) to achieve a higher compression ratio. In [12], GA is applied to find the optimum code-book for Vector Quantization (VQ) compression methods. In [13], PSO is used to find a better quantization table for image compression.…”

Section: Introductionmentioning

confidence: 99%

An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization

Ahmadi

Javaid

Salari

2015

Signal Processing: Image Communication

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“…In order to solve these problems, many novel algorithms are put forward, such as ant colony clustering algorithm, genetic algorithm (GA) and kernel fuzzy learning algorithm [2][3][4][5][6] . In Reference [2] ant colony clustering algorithm is used to generate codebook.…”

Section: Introductionmentioning

confidence: 99%

“…The hybrid method not only improves the quality of codebook but also speed algorithm convergence. Reference [4] puts forward a novel codebook generation algorithm using a combined scheme of principal component analysis and genetic algorithm. The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA.…”

Section: Introductionmentioning

confidence: 99%

Optimal Codebook Design Based on Ant Colony Clustering and Genetic Algorithms

Su¹,

Xiu²

2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

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-Codebook design plays an important role in the performance of signal processing based on vector quantization (VQ) just as speech coding, data compression and pattern recognition. LBG is one of the most effective algorithms and is widely used in codebook generation. Some problems still exist while its performance is remarkable. The LBG algorithm is easy to fall into local optimum. Usually there is strong correlation between the best solution and the initial selection for codebook design. It means that the quantization performance of codebooks from the same training data may varies in a certain range. There is also a certain probability for the algorithm to generate empty voronoi cell. In order to solve these problems, a novel algorithm based on ant colony clustering algorithm and genetic algorithm is proposed in this paper. The new algorithm takes advantage of the excellent global optimal searching ability of genetic algorithm. At the same time, the ant colony clustering algorithm is combined into the process. The dynamic change of the searching direction is adopted during crossover stage. The simulation results of line spectrum frequency parameters in mixed linear excitation prediction (MELP) show that the proposed algorithm is more efficient in its quantization performance compared to that of the LBG and genetic algorithms. Meanwhile, it has good stability in quantization performance.

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Efficient vector quantization using genetic algorithm

Cited by 26 publications

References 21 publications

Optimized tile-based texture synthesis

Optimized tile-based texture synthesis

An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization

Optimal Codebook Design Based on Ant Colony Clustering and Genetic Algorithms

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