Abstract
In this paper, we make an experimental study of some techniques of image compression based on artificial neural networks, particularly algorithm based on back-propagation gradient error [5]. We also present a new hybrid method based on the use of a multilayer perceptron which combines hierarchical and adaptative schemes. The idea is to compute in a parallel way, the back propagation algorithm on an adaptative neural network that uses sub-neural networks with a hierarchical structure to classify the image blocks in entry according to their activity. The results come from the Yann Le Cun database [7], and show that the proposed hybrid method gives good results in some cases.
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Kouamo, S., Tangha, C. (2013). Image Compression with Artificial Neural Networks. In: Herrero, Á., et al. International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions. Advances in Intelligent Systems and Computing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33018-6_53
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DOI: https://doi.org/10.1007/978-3-642-33018-6_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33017-9
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