Abstract
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.
Highlights
Nowadays images are very important elements in everyday communication
At this point of the WATCQ algorithm, a tree has been defined with q nodes in the second level, each representing a color of the quantized palette defined by the Greedy orthogonal bi-partitioning method (GOBP) algorithm
It can be considered that they compute pseudo-centroids, since the values are computed as the iterations of the algorithm progress and take into account the color of all the ants connected to each subtree and the centroid of a box of the partition defined by GOBP
Summary
Nowadays images are very important elements in everyday communication. Social networks, web pages, reports, e-books or electronic documents include many images that must be stored, transmitted and displayed. Román Gallego: Hybrid Color Quantization Algorithm That Combines the GOBP Method With Artificial Ants into boxes, each of the resulting boxes defining a color of the quantized palette [17] This method is very fast because the values used during the iterative process are calculated only once before the process begins. It should be taken into account that GOBP generates better images than most of the color quantization methods mentioned above, as shown by the computational results included in several articles [27]–[33]. GOBP has been chosen as starting point for two main reasons: it generates good quantized images (better than ATCQ in most cases) and is very fast Both features allow to define a new rapid method that considers a good quantized palette as a starting point.
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