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首页> 外文期刊>International Journal of Applied Metaheuristic Computing >New Artificial Neural Network Models for Bio Medical Image Compression: Bio Medical Image Compression
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New Artificial Neural Network Models for Bio Medical Image Compression: Bio Medical Image Compression

机译:用于生物医学图像压缩的新的人工神经网络模型:生物医学图像压缩

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This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.
机译:本文介绍了一种使用前馈反向传播神经网络(NNs)的图像压缩方法。在过去的十年中,图像压缩领域取得了显着进展。图像压缩去除图像数据中的冗余信息是解决海量数据存储和数据传输问题的解决方案。神经网络通过其生成内部数据表示的能力,为解决图像压缩问题提供了一种新颖的解决方案。比较了具有不同压缩比和不同块大小的各种前馈反向传播训练算法。学习方法,Levenberg Marquardt(LM)算法和Gradient Descent(GD)已用于执行网络体系结构的训练,最后,使用医学图像根据MSE和PSNR评估了性能。使用这两种算法获得的解压缩结果是根据PSNR和MSE以及性能图和回归图计算得出的,从中可以看出LM算法比GD算法给出的结果更准确。

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