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Comparative Performance Analysis of Optimization Techniques on Vector Quantization for Image Compression

机译:图像压缩矢量量化优化技术的比较性能分析

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摘要

>Linde-Buzo-Gray (LBG) Vector Quantization (VQ), technically generates local codebook after many runs on different sets of training images for image compression. The key role of VQ is to generate global codebook. In this paper, we present comparative performance analysis of different optimization techniques. Firefly and Cuckoo search generate a near global codebook, but undergoes problem when non-availability of brighter fireflies and convergence time is very high respectively. Hybrid Cuckoo Search (HCS) algorithm was developed and tested on four benchmark functions, that optimizes the LBG codebook with less convergence rate by taking McCulloch's algorithm based levy flight and variant of searching parameters. Practically, we observed that Bat algorithm (BA) peak signal to noise ratio is better than LBG, FA, CS and HCS in between 8 to 256 codebook sizes. The convergence time of BA is 2.4452, 2.734 and 1.5126 times faster than HCS, CS and FA respectively.
机译: > Linde-Buzo- Gray(LBG)矢量量化(VQ),在对不同组训练图像进行多次运行以进行图像压缩后,从技术上讲会生成本地码本。 VQ的关键作用是生成全局码本。在本文中,我们介绍了不同优化技术的比较性能分析。 Firefly和Cuckoo搜索会生成接近全局的密码本,但是当明亮的萤火虫的不可用性和收敛时间非常高时会遇到问题。混合布谷鸟搜索(HCS)算法是在四个基准功能上开发和测试的,通过采用基于McCulloch算法的征航和搜索参数变体来优化LBG码本,且收敛速度较低。实际上,我们观察到Bat算法(BA)的峰值信噪比在8至256码本大小之间优于LBG,FA,CS和HCS。 BA的收敛时间分别比HCS,CS和FA快2.4452、2.734和1.5126倍。

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