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2018 | OriginalPaper | Chapter

Training Feed-Forward Neural Networks Employing Improved Bat Algorithm for Digital Image Compression

Author : Adis Alihodzic

Published in: Large-Scale Scientific Computing

Publisher: Springer International Publishing

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Abstract

Training of feed-forward neural networks is a well-known and a vital optimization problem which is used to digital image lossy compression. Since the inter-pixel relationship in the picture is highly non-linear and unpredictive in the absence of a prior knowledge of the picture itself, it has shown that the neural networks combined with metaheuristics can be very efficient optimization method for image compression issues. In this paper, we propose an improved bat algorithm for training the input-output weights of the network which contains input-output layers of the equal sizes and a hidden layer of smaller size in-between. It has applied on five standard digital images. From the experimental analysis, it can be shown that the proposed method produces an acceptable quality of the compressed image as well as a good ratio of compression.

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Metadata
Title
Training Feed-Forward Neural Networks Employing Improved Bat Algorithm for Digital Image Compression
Author
Adis Alihodzic
Copyright Year
2018
Book
Large-Scale Scientific Computing

Print ISBN: 978-3-319-73440-8

Electronic ISBN: 978-3-319-73441-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-73441-5_33

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