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Fixed bit-rate image compression using a parallel-structure multilayer neural network

机译:使用并行结构多层神经网络的固定比特率图像压缩

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Picture compression algorithms, using a parallel structure of neural networks, have recently been described. Although these algorithms are intrinsically robust, and may therefore be used in high noise environments, they suffer from several drawbacks: high computational complexity, moderate reconstructed picture qualities, and a variable bit-rate. In this paper, we describe a simple parallel structure in which all three drawbacks are eliminated: the computational complexity is low, the quality of the decompressed picture is high, and the bit-rate is fixed.
机译:最近已经描述了使用神经网络的并行结构的图片压缩算法。尽管这些算法本质上是鲁棒的,因此可以在高噪声环境中使用,但它们具有以下缺点:计算复杂度高,重构的图像质量适中以及比特率可变。在本文中,我们描述了一种简单的并行结构,其中消除了所有三个缺点:计算复杂度低,解压缩图片的质量高并且位速率固定。

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