Optimization Based Vector Quantization for Data Reduction in Multimedia Applications

Kavitha, V.; Kanchana, M.; Gobinathan, Balasubramanian; Sekar, K. R.; Sikkandar, Mohamed Yacin

doi:10.32604/iasc.2022.018358

Cited by 2 publications

(3 citation statements)

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“…Efficient coding of signals is an essential process in several areas and applications, such as mobile communications, streaming services and image storage, among others. VQ is an efficient coding technique that aims to reduce the number of bits required to represent a signal [ 4 , 15 , 16 , 17 ].…”

Section: Vector Quantizationmentioning

confidence: 99%

“…M-FA-LBG proposes to precede the repositioning of fireflies with the calculation of the centroids of each Voronoi region. In this way, the codebook is updated by calculating the new centroids of the Voronoi regions, according to Equation (5) and then updating the position of the fireflies, according to Equations (17) and (19). The objective of introducing the centroid calculation in the codebook update step in M-FA-LBG is to allow a greater influence of the training set on the codebook design, aiming to minimize the distortion introduced when representing the training vectors by the corresponding code vectors.…”

Section: Modified Fa-lbg Algorithmmentioning

confidence: 99%

“…. , N, except the one with greatest brightness, according to Equation (17). If W (a) is the brightest firefly, update its position according to Equation ( 19).…”

mentioning

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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