Algoritmo PSO Modificado Aplicado ao Projeto de Quantizadores Vetoriais

Severo, Verusca; Leitão, Herbert A. de Sá; Lima, Juliano B.; Lopes, Waslon T. A.; Madeiro, Francisco

doi:10.21528/cbic2015-072

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The M-PSO-LBG (Modified Particle Swarm Optimization—Linde–Buzo–Gray), proposed by Severo et al in [ 6 ], was derived from changes made to the PSO-LBG [ 34 ] allowing greater influence of the training set in codebook design. The changes concern the way the algorithm is initialized, the codebook training stage, that is, the codevectors update stage, and the definition of the algorithm’s stopping criterion.…”

Section: Swarm Techniques Applied To Vqmentioning

confidence: 99%

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On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design

Severo,

Ferreira,

Spencer

et al. 2024

Sensors

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Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm intelligence. Initialization of the Linde–Buzo–Gray (LBG) algorithm, which is the most popular VQ codebook design algorithm, is a step that directly influences VQ performance, as the convergence speed and codebook quality depend on the initial codebook. A widely used initialization alternative is random initialization, in which the initial set of codevectors is drawn randomly from the training set. Other initialization methods can lead to a better quality of the designed codebooks. The present work evaluates the impacts of initialization strategies on swarm intelligence algorithms for codebook design in terms of the quality of the designed codebooks, assessed by the quality of the reconstructed images, and in terms of the convergence speed, evaluated by the number of iterations. Initialization strategies consist of a combination of codebooks obtained by initialization algorithms from the literature with codebooks composed of vectors randomly selected from the training set. The possibility of combining different initialization techniques provides new perspectives in the search for the quality of the VQ codebooks. Nine initialization strategies are presented, which are compared with random initialization. Initialization strategies are evaluated on the following algorithms for codebook design based on swarm clustering: modified firefly algorithm—Linde–Buzo–Gray (M-FA-LBG), modified particle swarm optimization—Linde–Buzo–Gray (M-PSO-LBG), modified fish school search—Linde–Buzo–Gray (M-FSS-LBG) and their accelerated versions (M-FA-LBGa, M-PSO-LBGa and M-FSS-LBGa) which are obtained by replacing the LBG with the accelerated LBG algorithm. The simulation results point out to the benefits of the proposed initialization strategies. The results show gains up to 4.43 dB in terms of PSNR for image Clock with M-PSO-LBG codebooks of size 512 and codebook design time savings up to 67.05% for image Clock, with M-FF-LBGa codebooks with size N=512, by using initialization strategies in substitution to Random initialization.

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Section: Swarm Techniques Applied To Vqmentioning

confidence: 99%

“…In recent years, a new family of algorithms has emerged from combining swarm intelligence with LBG [ 6 , 7 , 8 ]. The versatility of swarm techniques allows them to be combined with the LBG algorithm and used in codebook design.…”

Section: Introductionmentioning

confidence: 99%

On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design

Severo,

Ferreira,

Spencer

et al. 2024

Sensors

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“…Como métricas de avalição do desempenho da QV aplicado a imagens digitais, utiliza-se o valor de Relação Sinal-Ruido de Pico (Peak Signal to Noise Ratio), definida por P SN R = 10 log 10 ( A QV tem seu desempenho ligado diretamente ao dicionário projetado [9,10]. Então, um bom dicionárioé extremamente importante para que o sinal reconstruído seja o mais parecido ao original.…”

Section: Quantização Vetorialunclassified

Inserção de Marca D’água Digital em Imagens Digitais Utilizando Quantização Vetorial com Particionamento do Dicionário por PSO

Canejo¹,

Galvão²,

Madeiro³

2015

Anais Do 12. Congresso Brasileiro De Inteligência Computacional

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Resumo-Segurança da informação envolvendo imagens digitaisé umaárea de estudo com problemas relevantes, dentre as quais podem ser citados esteganografia, cifragem de imagens e marca d'água digital. A quantização vetorial, quando aplicadà a marca d'água, envolve geralmente o particionamento de dicionário. No presente trabalho, avalia-se o desempenho do algoritmo PSO (particle swarm optimization) nesse particionamento. Mostra-se que o PSO leva a marcas d'água mais robustas quando comparadasàs marcas obtidas com o uso de algoritmo genético.

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Algoritmo PSO Modificado Aplicado ao Projeto de Quantizadores Vetoriais

Cited by 2 publications

References 12 publications

On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design

On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design

Inserção de Marca D’água Digital em Imagens Digitais Utilizando Quantização Vetorial com Particionamento do Dicionário por PSO

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