Color Reduction and Quantization using Kohonan Self Organizing Neural Networks and K-means algorithms
DOI:
https://doi.org/10.37375/foej.v3i1.2588Keywords:
Color Reduction and Quantization, Kohonan Self Organizing Neural Networks and K-meansAbstract
Color quantization aims to decrease the number of unique colors in photographs while maintaining a high level of color fidelity in comparison to the original images. The quality of the final image is strongly influenced by the color scheme you choose, so getting it right is essential. Identifying the clusters that best capture the colors in an image is the goal of the color quantization issue, which may also be thought of as a clustering problem. In this study, Kohonen Self-Organizing Neural Networks (SOM) and k-means algorithms were implemented to determine the color reduction and quantization of some images and then compare the results against k-means clustering. The findings indicate that when it comes to identifying k values in color reduction and quantization, the k-means method performs better than SOM.
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