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Growing Neural Forest-Based Color Quantization Applied to RGB Images

Growing Neural Forest-Based Color Quantization Applied to RGB Images

Jesús Benito-Picazo, Ezequiel López-Rubio, Enrique Domínguez
Copyright: © 2017 |Volume: 7 |Issue: 3 |Pages: 13
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522522997|DOI: 10.4018/IJCVIP.2017070102
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MLA

Benito-Picazo, Jesús, et al. "Growing Neural Forest-Based Color Quantization Applied to RGB Images." IJCVIP vol.7, no.3 2017: pp.13-25. http://doi.org/10.4018/IJCVIP.2017070102

APA

Benito-Picazo, J., López-Rubio, E., & Domínguez, E. (2017). Growing Neural Forest-Based Color Quantization Applied to RGB Images. International Journal of Computer Vision and Image Processing (IJCVIP), 7(3), 13-25. http://doi.org/10.4018/IJCVIP.2017070102

Chicago

Benito-Picazo, Jesús, Ezequiel López-Rubio, and Enrique Domínguez. "Growing Neural Forest-Based Color Quantization Applied to RGB Images," International Journal of Computer Vision and Image Processing (IJCVIP) 7, no.3: 13-25. http://doi.org/10.4018/IJCVIP.2017070102

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Abstract

Although last improvements in both physical storage technologies and image handling techniques have eased image managing processes, the large amount of information handled nowadays constantly demands more efficient ways to store and transmit image data streams. Among other alternatives for such purpose, the authors find color quantization, which consists of color indexing for minimal perceptual distortion image compression. In this context, artificial intelligence-based algorithms and more specifically, Artificial Neural Networks, have been consolidated as a powerful tool for unsupervised tasks, and therefore, for color quantization purposes. In this work, a novel approach to color quantization is presented based on the Growing Neural Forest (GNF), which is a Growing Neural Gas (GNG) variation where a set of trees is learnt instead of a general graph. Experimental results support the use of GNF for image quantization tasks where it overcomes other self-organized models including SOM, GHSOM and GNG. Future work will include more datasets and different competitive models to compare to.

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