Abstract
We present the packet wavelet coder implemented with Cellular Neural Network architecture as an example of the applications of cellular neural networks. This paper also demonstrates how the cellular neural universal machine (CNNUM) architecture can be extended to image compression. The packet wavelet coder performs the operation of image compression, aided by CNN architecture. It uses the highly parallel nature of the CNN structure and its speed outperforms traditional digital computers. In packet wavelet coder, an image signal can be analyzed by passing it through an analysis filter banks followed by a decimation process, according to the rules of packet wavelets. The Simulation results indicate that the quality of the reconstructed image is improvised by using packet wavelet coding scheme.
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Venkateswaran, N., Vignesh, J., Kumar, S.S., Rahul, S., Bharadwaj, M. (2007). A Frequency Adaptive Packet Wavelet Coder for Still Images Using CNN. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_24
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DOI: https://doi.org/10.1007/978-3-540-77368-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77367-2
Online ISBN: 978-3-540-77368-9
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