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首页> 外文期刊>Journal of ambient intelligence and humanized computing >An efficient codebook generation using firefly algorithm for optimum medical image compression
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An efficient codebook generation using firefly algorithm for optimum medical image compression

机译:使用Firefly算法进行最佳医学图像压缩的有效码本生成

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摘要

In recent times, the medical imaging becomes an indispensable tool in clinical practice. Due to the large volume of medical images, compression is needed to lessen the redundancies in the image and also to represent the image in shorter manner for effective transmission. In this paper, Linde-Buzo-Gray (LBG) algorithm was developed with vector quantization (VQ) for compressing the images, and it results in decent image quality. To further increase the image quality, optimization techniques [particle swarm optimization (PSO) and firefly algorithm (FA)] were used in LBG method to optimize the codebook for generating the global codebook. In the proposed work, LBG method was used to get the local codebooks and the obtained local codebooks were optimized by utilizing PSO. The optimized codebooks from PSO were again optimized by using FA that results in good quality of the image. In the experimental phase, the performance of the proposed work was compared with individual optimization techniques like PSO and FA. From the experimental study, the proposed work showed 1.2-6 dB improvement in image compression related to other existing approaches.
机译:最近,医学成像成为临床实践中不可或缺的工具。由于大量的医学图像,需要压缩来减少图像中的冗余,并且还以更短的方式表示图像以便有效传输。在本文中,用矢量量化(VQ)开发了Linde-Buzo-灰色(LBG)算法,用于压缩图像,并导致体积的图像质量。为了进一步提高图像质量,在LBG方法中使用了优化技术[粒子群优化(PSO)和Firefly算法(FA)],以优化用于生成全局码本的码本。在所提出的工作中,使用LBG方法来获取本地码本,并通过使用PSO来优化所获得的本地码本。通过使用FA,再次优化PSO的优化码本,其导致图像质量良好。在实验阶段,将所提出的工作的性能与PSO和FA这样的单独优化技术进行比较。从实验研究中,拟议的工作表现为与其他现有方法相关的图像压缩的1.2-6 dB改善。

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