Algorithm For Color Images Research Articles

Reversible data hiding based on prediction error difference expansion of ternary components

To avoid the drawback of Difference Expansion ( DE) algorithm which must embed a location map and the quality decline of stego-images from excessive modulation to pixels, a reversible data hiding algorithm for color images was proposed. The algorithm made use of correlation of color components to decrease difference and use smaller expansion to change the pixels value of some components. The expansion direction was determined by the quantity of overflow and underflow pixels. The embedding scheme adopted the adjusting method of single direction pixels value to prevent overflow or underflow, and embedded a few adjusting value instead of location map, so the embedding capacity was increased; finally, improved the difference histogram shifting technique to control the capacity and distortion. According to the sequence of critical value, the adjusted pixels value could be restored. The experimental results show the embedding capacity and the quality of stego-image are significantly improved in comparison with other new or classical algorithms.

基于非抽样Contourlet变换的彩色图像数字水印算法

基于非抽样Contourlet变换的彩色图像数字水印算法

Fuzzy error diffusion

Quantization errors are generally hidden by performing a dithering operation on the image. A common method is to utilize error diffusion. However, this method is prone to error accumulation, resulting in color impulses and streaks. This paper presents a new approach to error diffusion dithering through a fuzzy error diffusion algorithm. In this method, the amount of error to be diffused is determined by considering the relative location of the pixel not only to the closest codebook vector, but to all other palette entries. The goal is to hide the quantization errors by error diffusion, while preventing the excess accumulation of errors. This is achieved through an attraction-repulsion schema according to a fuzzy membership function. We also explored methods to speed up the fuzzy error diffusion process through a L-filter approach by determining a fixed set of membership values. We have implemented the fuzzy error diffusion algorithm for color images and achieved drastic improvements, resulting in superior quality dithered images and significantly lower mean squared error values. A different error measure modeling the characteristic of the human visual system also indicates the superiority of our method.