Severo Verusca, Ferreira Felipe B S, Spencer Rodrigo, Nascimento Arthur, Madeiro Francisco
Polytechnic School of Pernambuco, University of Pernambuco, Recife 50720-001, Brazil.
Engineering Campus, Rural Federal University of Pernambuco, Cabo de Santo Agostinho 54518-430, Brazil.
Sensors (Basel). 2024 Apr 19;24(8):2606. doi: 10.3390/s24082606.
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.
矢量量化(VQ)是一种应用广泛的技术。例如,它可用于图像压缩。VQ的码本设计对量化信号的质量具有重要意义,并且可以从群体智能的应用中受益。林德 - 布佐 - 格雷(LBG)算法是最流行的VQ码本设计算法,其初始化是直接影响VQ性能的一个步骤,因为收敛速度和码本质量取决于初始码本。一种广泛使用的初始化方法是随机初始化,即初始码向量集是从训练集中随机抽取的。其他初始化方法可以带来质量更好的设计码本。本研究从设计码本的质量(通过重建图像的质量评估)和收敛速度(通过迭代次数评估)两方面评估初始化策略对用于码本设计的群体智能算法的影响。初始化策略包括将文献中的初始化算法得到的码本与由从训练集中随机选择的向量组成的码本相结合。组合不同初始化技术的可能性为寻找高质量的VQ码本提供了新的视角。本文提出了九种初始化策略,并将它们与随机初始化进行比较。基于群体聚类的码本设计算法对初始化策略进行评估,这些算法包括:改进的萤火虫算法 - 林德 - 布佐 - 格雷(M - FA - LBG)、改进的粒子群优化算法 - 林德 - 布佐 - 格雷(M - PSO - LBG)、改进的鱼群搜索算法 - 林德 - 布佐 - 格雷(M - FSS - LBG)以及它们的加速版本(M - FA - LBGa、M - PSO - LBGa和M - FSS - LBGa),加速版本是通过用加速的LBG算法替换LBG算法得到的。仿真结果表明了所提出的初始化策略的优势。结果显示,对于大小为512的M - PSO - LBG码本,图像“时钟”的峰值信噪比(PSNR)增益高达4.43 dB;对于大小为N = 512的M - FF - LBGa码本,通过使用初始化策略替代随机初始化,图像“时钟”的码本设计时间节省高达67.05%。