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首页> 外文期刊>Advanced Science Letters >Lossy Image Compression Based on Vector Quantization Using Artificial Bee Colony and Genetic Algorithms
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Lossy Image Compression Based on Vector Quantization Using Artificial Bee Colony and Genetic Algorithms

机译：基于矢量量化使用人造蜂菌落和遗传算法的有损图像压缩

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摘要

In recent years, the volume of image data that are being employed for Internet and other applications has been increasing at an enormous rate. To cope up with the existing limitations on the storage space and the network bandwidth, it has become necessary to develop more efficient compressiontechniques. Lossy compression is more popular compared to lossless compression as it is more widely used in a variety of applications. In lossy compression, it is necessary to maintain the quality of the reconstructed image when the compression scheme is applied. Thus, compression ratio andthe reconstructed image quality are the two important parameters based on which the performance of a lossy compression scheme is judged. In this paper, a new lossy compression scheme is proposed which employs codebook concept. For the generation of the codebook, a new technique denoted asABC-GA technique which is a combination of artificial bee colony and genetic algorithms is employed. The performance of the proposed compression scheme is evaluated using two different types of databases, namely, CLEF med 2009 and standard images (Lena, Barbara etc.). The experimental resultsshow that the proposed technique performs better than the existing algorithms yielding average PSNR values of 43.05, 41.58, 40.06, 37.41, 35.24 for compression ratios 10, 20, 40, 60, 80 respectively in the case of standard images.
机译：近年来，用于互联网和其他应用的图像数据的体积以巨大的速度增加。为了应对存储空间和网络带宽的现有限制，已经有必要开发更有效的压缩技术。与无损压缩相比，损坏压缩更流行，因为它更广泛地用于各种应用。在有损压缩中，在应用压缩方案时必须保持重建图像的质量。因此，压缩比和重建的图像质量是基于该重构的两个重要参数，其判断有损压缩方案的性能。在本文中，提出了一种采用码本概念的新的有损压缩方案。对于代码本，采用了一种新的技术，其表示作为人造群落和遗传算法的组合的ASABC-GA技术。使用两种不同类型的数据库，即Clef Med 2009和标准图像（Lena，Barbara等）来评估所提出的压缩方案的性能。所提出的技术在标准图像的情况下，所提出的技术的实验结果表现优于产生43.05,41.58,40.06,37.41,35.24的平均PSNR值的现有算法。

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