An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps

Table 2

Various color quantization methods [1].

Methods	Features of methods
POP [10]	(i) One of the simplest methods.
	(ii) 16 × 16 × 16 color histogram using 4 bits per channel uniform quantization.
	(iii) K most-frequent colors in the color histogram are used for quantization.
MC [10]	(i) 32 × 32 × 32 color histogram using 5 bits per channel uniform quantization.
	(ii) It makes cubes that include all of the histogram.
	(iii) It repeatedly splits the cubes that have the greatest number of colors until K cubes are obtained.
OCT [9]	(i) Tree structure with up to 8 nodes as children, which can represent all colors in an image within an 8-level tree.
	(ii) Color distribution is represented using octree, which then prunes the nodes until K nodes remain.
KM [11, 14, 15]	(i) It starts with K random clusters.
	(ii) All of the input data are assigned to the cluster that has the minimum distance within the data.
	(iii) The centroid of the cluster is calculated as the average of the assigned data.
ART2 [12]	(i) Unsupervised learning model.
	(ii) It creates new clusters depending on a vigilance test.
	(iii) The palette color is chosen from the centroids of the most-frequent clusters.
SOM [1–5]	(i) Unsupervised learning model.
	(ii) One-dimensional self-organizing map with K neurons.
	(iii) It designates the minimum distance node as the “winner” node and then updates the weights of the winner node and neighbor nodes.
	(iv) It repeats the process until the sum of the weight change is less than a certain threshold.