基于PCA和MLP神经网络改进技术的图像压缩

Vilas H. Gaidhane, Vijander Singh, Mahendra Kumar
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引用次数: 32

摘要

计算机图像包含大量数据,因此需要在存储器中存储更多的空间。压缩后的图像需要更少的内存存储空间和更短的传输时间。本文将前馈-反向传播神经网络方法与主成分分析技术相结合,用于图像压缩。结果表明,主成分分析的结果并不令人满意。为了改善这些结果,作者提出了另一种效果更好的方法。该技术中使用的人工神经网络(ANN)是通过考虑不同数量的隐藏神经元、epoch和重建图像与原始图像的比较来训练的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Compression Using PCA and Improved Technique with MLP Neural Network
Computer images consist of large data and hence require more space to store in the memory. The compressed image requires less storing space of memory and less time to transmit. In this paper, feed forward back propagation neural network method with PCA technique is used for image compression. It is found that the results using PCA techniques are not satisfactory. To improve these results author suggest another technique which has better results. The Artificial Neural Network (ANN) used in these technique is trained by considering the different numbers of hidden neurons, epoch and reconstructed image compared with original image.
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