A New Compression Technique Using an Artificial Neural Network
Brijesh VermaSchool ofInformation Technology, Faculty of Information and Communication Technology, Griffith University, Gold Coast Campus, PMB 50, Gold Coast Mail Centre, OLD 9726, AustraliaMichael BlumensteinSchool ofInformation Technology, Faculty of Information and Communication Technology, Griffith University, Gold Coast Campus, PMB 50, Gold Coast Mail Centre, OLD 9726, AustraliaSanjeev R. KulkarniSchool ofInformation Technology, Faculty of Information and Communication Technology, Griffith University, Gold Coast Campus, PMB 50, Gold Coast Mail Centre, OLD 9726, Australia
1999en
ABI
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In this paper, we present a direct solution method based neural network for image compression. The proposed technique includes steps to break down large images into smaller windows and eliminate redundant information. Furthermore, the technique employs a neural network that is trained by a non-iterative, direct solution method. An error backpropagation algorithm is also used to train the neural network, and both training algorithms are compared. The proposed technique has been implemented in C on the SP2 Supercomputer. A number of experiments have been conducted. The results obtained, such as compression ratio and transfer time of the compressed images are presented in this paper.
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