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Fuzzy-VQ image compression based hybrid PSOGSA optimization algorithm

机译:基于模糊VQ图像压缩的混合PSOGSA优化算法

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The transmission speed of big data in multimedia, social networking, and web services, can be enhanced by image compression technology. Fuzzy vector quantization (VQ) image compression is a significant tool for achieving a codebook to illuminate lineaments of big data. A functionality combination of PSO and GSA algorithms, with parallel running, have been used to design a fuzzy-VQ image compression system. The improvement of the compressed image quality has been executed by carrying out suitable parameters selection using the proposed algorithm. Comparative study between sophisticated learning schemes and Linde-Buzo-Gray (LBG) based VQ learning process has been introduced. The proposed algorithms provide an achievement in the behavior of pure image compression.
机译:通过图像压缩技术可以增强多媒体,社交网络和Web服务中大数据的传输速度。模糊矢量量化(VQ)图像压缩是实现码本以解释大数据的谱系的重要工具。使用并行运行的PSO和GSA算法的功能组合已被用于设计模糊VQ图像压缩系统。通过使用所提出的算法执行合适的参数选择,已经执行了压缩图像质量的改进。已经介绍了复杂的学习方案和基于LINDE-BUZO-灰度(LBG)的VQ学习过程的比较研究。所提出的算法提供了纯图像压缩行为的成就。

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