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A color quantization technique that combines the operations of two existing methods is proposed. The first method considered is the Greedy orthogonal bi-partitioning method. This is a very popular technique in the color quantization field that can obtain a solution quickly. The second method, called Ant-tree for color quantization, was recently proposed and can obtain better images than some other color quantization techniques. The solution described in this article combines both methods to obtain images with good quality at a low computational cost. The resulting images are always better than those generated by each method applied separately. In addition, the results also improve those obtained by other well-known color quantization methods, such as Octree, Median-cut, Neuquant, Binary splitting or Variance-based methods. The features of the proposed method make it suitable for real-time image processing applications, which are related to many practical problems in diverse disciplines, such as medicine and engineering.

This article proposes a color quantization strategy that combines two color quantization methods: Binary Splitting and Ant tree for Color Quantization. This solution combines a splitting method, which is faster, and a clustering-based method, which generates better quantized images. Given that time is a fundamental factor when considering a method for real-time applications, the proposed strategy attempts to exploit both of these methods for obtaining good quantized images with a low computational cost. The result of this approach not only generates better images than when Binary Splitting and Ant tree for Color Quantization are applied separately, but also helps to improve other methods frequently used for color quantization such as Wu’s method, Octree, Variance-based method and Neuquant.