Path: Top -> Journal -> Telkomnika -> 2015 -> Vol 13, No 1: March
An Image Compression Scheme Based on Fuzzy Neural Network
An Image Compression Scheme Based on Fuzzy Neural Network
Journal from gdlhub / 2016-11-09 12:21:20Oleh : Bo Wang, Yubin Gao, Telkomnika
Dibuat : 2015-03-01, dengan 1 file
Keyword : image compression, fuzzy theory, neural network
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1270
Image compression technology is to compress the redundancy between the pixels to reduce the transmission broadband and storage space by using the correlation of the image pixels. Fuzzy neural network effectively integrates neural network technology and fuzzy technology; combines learning, self-adaptivity, imagination and identity and uses rule-based reasoning and fuzzy information processing in the nodes; thus greatly improving the transparency of fuzzy neural network. This paper mainly investigates the applications of fuzzy neural network in image compression and realizes the image compression and reconstruction of fuzzy neural network. It is demonstrated in the simulation experiment that the image compression algorithm based on fuzzy neural network has significant advantages in training speed, compression quality and robustness.
Deskripsi Alternatif :Image compression technology is to compress the redundancy between the pixels to reduce the transmission broadband and storage space by using the correlation of the image pixels. Fuzzy neural network effectively integrates neural network technology and fuzzy technology; combines learning, self-adaptivity, imagination and identity and uses rule-based reasoning and fuzzy information processing in the nodes; thus greatly improving the transparency of fuzzy neural network. This paper mainly investigates the applications of fuzzy neural network in image compression and realizes the image compression and reconstruction of fuzzy neural network. It is demonstrated in the simulation experiment that the image compression algorithm based on fuzzy neural network has significant advantages in training speed, compression quality and robustness.
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