This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

A new method for color image authentication and recovery is proposed in this paper. To achieve this goal, the least significant bit (LSB) and the second LSB of the spatial domain were selected to embed the watermark. The watermark consists of two parts: authentication data and recovery data made by block truncation code and bitmap. According to the experimental results, the image embedded within the authentication data and recovery data can still preserve high image quality. The results also reveal that the tampered images can be successfully recovered with acceptable visual quality.

Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is efficiently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption.

A polygonal approximation method based on the new artificial bee colony (NABC) algorithm is proposed in this paper. In the present method, a solution is represented by a vector, and the objective function is defined as the integral square error between the given curve and its corresponding polygon. The search process, including the employed bee stage, the onlooker bee stage, and the scout bee stage, has been constructed for this specific problem. Most experiments show that the present method when compared with the DE-based method can obtain superior approximation results with less error norm with respect to the original curves.