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Using feed forward multilayer neural network and vector quantization as an image data compression technique

机译:使用前馈多层神经网络和矢量量化作为图像数据压缩技术

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Single hidden layer feed forward neural networks with different number of hidden neurons are used for image data compression. A subimage of size 4/spl times/4 pixels forms the input vector of size 16 pixels. The hidden vector, which is the output of the hidden layer whose size is smaller than that of the input vector represents the compressed form of the image data. The hidden vector is transmitted by a vector quantizer with codebook of 256 codevectors which corresponds to a bit rate of 0.5 bit/pixel. The reconstructed subimage, at the receiver, is obtained from the output layer which consists of 16 neurons. Good reconstructed images are obtained with a PSNR of about 30 dB for the in-training set image (Lena) and 27 dB for the outside-training set image (Boats).
机译:具有不同数目的隐藏神经元的单个隐藏层前馈神经网络用于图像数据压缩。大小为4 / spl乘以/ 4像素的子图像形成大小为16像素的输入向量。隐藏向量是隐藏层的输出,其大小小于输入向量的大小,它表示图像数据的压缩形式。隐藏的向量由具有256个码向量的码本的向量量化器传输,该码本对应于0.5位/像素的比特率。接收器处的重构子图像是从由16个神经元组成的输出层获得的。获得了良好的重建图像,对于训练中的设置图像(Lena),PSNR约为30 dB;对于外部训练的设置图像(船),PSNR约为27 dB。

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