A neural networks approach to image data compression

Authors:
Hamdy S. Soliman;Mohammed Omari
Affiliations:
Computer Science Department, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA;Computer Science Department, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
Venue:
Applied Soft Computing
Year:
2006

Citing 4
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Abstract

We present a novel neural model for image compression called the direct classification (DC) model. The DC is a hybrid between a subset of the self-organizing Kohonen (SOK) model and the adaptive resonance theory (ART) model. The DC is a fast and efficient neural classification engine. The DC training utilizes the accuracy of the winner-takes-all feature of the SOK model and the elasticity/speed of the ART1 model. The DC engine has experimentally achieved much better results than the state-of-the-art peer image compression techniques (e.g., JPEG2000 and DjVu wavelet technology) especially in the domains of colored documents and still satellite images. We include a comprehensive analysis of the most important parameters of our DC system and their effects on system performance.