Journal article Open Access

Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons

G.G.H.M.T.R. Bandara; R. Siyambalapitiya

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    <subfield code="a">Image Compression, Deep Learning, Autoencoder, Backpropagation Algorithm.</subfield>
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    <subfield code="u">Department of Statistics &amp; Computer  Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri  Lanka.</subfield>
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    <subfield code="u">Department of Statistics &amp; Computer  Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri  Lanka.</subfield>
    <subfield code="a">G.G.H.M.T.R. Bandara</subfield>
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    <subfield code="a">Deep Autoencoder-Based Image Compression  using Multi-Layer Perceptrons</subfield>
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    <subfield code="a">&lt;p&gt;The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of the proposed network has been done for various block sizes and different saving percentages of various kinds of images by using the backpropagation algorithm. The output of the middle-hidden layer will be the compressed representation for each block of the image. The proposed model has been implemented using Python, Keras, and Tensorflow backend.&lt;/p&gt;</subfield>
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