Please use this identifier to cite or link to this item: http://www.repositorio.ufc.br/handle/riufc/19036
Title in Portuguese: Artificial neural networks for compression of gray scale images: a benchmark
Author: Souza, Osvaldo de
Cortez, Paulo Cesar
Silva, Francisco de Assis Tavares Ferreira da
Keywords: Artificial neural network
Digital image compression
Neural network benchmark
Morphological neural network
Vector quantization
Mathematical morphology
Issue Date: 2013
Publisher: SBC
Citation: SOUSA, Osvaldo de; CORTEZ, Paulo Cesar; SILVA, Francisco de Assis Tavares Ferreira da. Artificial neural networks for compression of gray scale images: a benchmark. In: National Meeting on Artificial and Computational Intelligence, 10., 2013, Fortaleza. Anais... Fortaleza: SBC, 2013.
Abstract: In this paper we present results for an investigation of the use of neural networks for the compression of digital images. The main objective of this investigation is the establishment of a ranking of the performance of neural networks with different architectures and different principles of convergence. The ranking involves backpropagation networks (BPNs), hierarchical back-propagation network (HBPN), adaptive back-propagation network (ABPN), a self-organizing maps (KSOM), hierarchically self-organizing maps (HSOM), radial basis function neural networks (RBF) and a supervised Morphological neural networks (SMNN). For the SMNN, considering that it is a neural network recently introduced, an explanation is presented for use in image compression. Gray scale image of Lena were used as the sample image for all network covered in this research. The best result is compression rate of 195.54 with PSNR = 22.97.
URI: http://www.repositorio.ufc.br/handle/riufc/19036
metadata.dc.type: Artigo de Evento
Appears in Collections:DCINF - Trabalhos apresentados em eventos

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