Use este identificador para citar ou linkar para este item: http://www.repositorio.ufc.br/handle/riufc/19036
Título: Artificial neural networks for compression of gray scale images: a benchmark
Autor(es): SOUZA, Osvaldo de
CORTEZ, Paulo Cesar
SILVA, Francisco de Assis Tavares Ferreira da
Palavras-chave: Artificial neural network
Digital image compression
Neural network benchmark
Morphological neural network
Vector quantization
Mathematical morphology
Data do documento: 2013
Editor: SBC
Citação: SOUZA, O.; CORTEZ, P. C.; SILVA, F. A. T. F. (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.
Descrição: 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.
URI: http://www.repositorio.ufc.br/handle/riufc/19036
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