Image Compression with Neural Networks Using Complexity Level of Images

H. Veisi, M. Jamzad
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引用次数: 12

Abstract

This paper presents a complexity-based image compression method using neural networks. In this method, different multi-layer perceptron ANNs are used as compressor and de-compressor. Each image is divided into blocks, complexity of each block is computed using complexity measure methods and one network is selected for each block according to its complexity value. Three complexity measure methods, called entropy, activity and pattern-based are used to determine the level of complexity in image blocks and their ability are evaluated and compared together. Selection of a network for each image block is based on its complexity value or the Best-SNR criterion. Best-SNR chooses one of the trained networks such that it results best SNR in compressing a block of input image. In our evaluations, best results, with PSNR criterion, are obtained when overlapping of blocks is allowed and choosing the networks in compressor is based on the Best-SNR criterion. In this case, the results demonstrate superiority of our method comparing with previous similar works and that of JPEG standard coding.
基于图像复杂度的神经网络图像压缩
提出了一种基于复杂度的神经网络图像压缩方法。在该方法中,使用不同的多层感知器ann作为压缩器和解压缩器。将图像分成块,利用复杂度度量方法计算每个块的复杂度,并根据复杂度值为每个块选择一个网络。采用熵、活动和基于模式的三种复杂性度量方法来确定图像块的复杂性水平,并对它们的能力进行了评价和比较。为每个图像块选择网络是基于其复杂度值或最佳信噪比标准。最佳信噪比选择一个经过训练的网络,使其在压缩输入图像块时产生最佳的信噪比。在我们的评估中,当允许块重叠并且基于最佳信噪比准则选择压缩器中的网络时,以PSNR准则获得最佳结果。在此案例中,与以往的同类工作和JPEG标准编码相比,证明了我们的方法的优越性。
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