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- W1617017211 abstract "lmage compression with neural networks is compared with the classical statistical principal components analysis (PCA) method. The neural networks considered here are the 3-layer and 5-layer Multilayer Perceptron (MLP) networks trained in auto-association mode with the back propagation learning algorithm, and the PCA network which is an adaptive way to solve the principal components. The mean square error and mean absolute error are compared for each method using a number of real-world test images. It is shown that in the image compression task using 8 x 8 image blocks, the 5-layer MLP can do better than the 3-layer MLP and PCA when the data is the same as used for training the networks, but its generalization ability is poor when the data is new. When the amount of training patterns is large enough, the generalization abilities are comparable but the training times for the MLP are prohibitively long. In both cases, PCA techniques give the best generalization results." @default.
- W1617017211 created "2016-06-24" @default.
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- W1617017211 date "2005-08-25" @default.
- W1617017211 modified "2023-09-24" @default.
- W1617017211 title "Image compression by neural networks: a comparison study" @default.
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- W1617017211 doi "https://doi.org/10.1109/ndsp.1993.767759" @default.
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