Unsupervised color quantization with the growing neural forest

Abstract

Image processing has become a very common application for artificial intelligence-based algorithms. More precisely, color quantization has become an important issue when it comes to supply efficient transmission and storage for digital images, which consists of color indexing for minimal perceptual distortion image compression. Artificial Neural Networks have been consolidated as a powerful tool for unsupervised tasks, and therefore, for color quantization purposes. In this work we present a novel color quantization approach 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. Its suitability for color quantization processes is supported by experimental results obtained, where the GNF outperforms other self-organizing models such as the GNG, GHSOM and SOM. As future work, more datasets and competitive models will be taken into account.