Lu Tzu-Chuen, Yang Ping-Chung, Jana Biswapati
Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan.
Department of Computer Science, Vidyasagar University, Midnapore 721102, India.
Entropy (Basel). 2021 May 8;23(5):577. doi: 10.3390/e23050577.
In 2018, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit matching (LSB matching) method. This method improved on the dual-image LSB matching method proposed by Lu et al. In Lu et al.'s scheme, there are seven situations that cannot be restored and need to be modified. Furthermore, the scheme uses two pixels to conceal four secret bits. The maximum modification of each pixel, in Lu et al.'s scheme, is two. To decrease the modification, Tseng et al. use one pixel to embed two secret bits and allow the maximum modification to decrease from two to one such that the image quality can be improved. This study enhances Tseng et al.'s method by re-encoding the modified rule table based on the probability of each hiding combination. The scheme analyzes the frequency occurrence of each combination and sets the lowest modified codes to the highest frequency case to significantly reduce the amount of modification. Experimental results show that better image quality is obtained using our method under the same amount of hiding payload.
2018年，曾等人提出了一种基于改进的最低有效位匹配（LSB匹配）方法的双图像可逆嵌入方法。该方法对卢等人提出的双图像LSB匹配方法进行了改进。在卢等人的方案中，存在七种无法恢复的情况，需要进行修改。此外，该方案使用两个像素来隐藏四个秘密位。在卢等人的方案中，每个像素的最大修改量为2。为了减少修改量，曾等人使用一个像素来嵌入两个秘密位，并使最大修改量从2减少到1，从而可以提高图像质量。本研究通过基于每个隐藏组合的概率对修改规则表进行重新编码，增强了曾等人的方法。该方案分析了每个组合的出现频率，并将最低修改码设置为出现频率最高的情况，以显著减少修改量。实验结果表明，在相同的隐藏信息量下，使用我们的方法可以获得更好的图像质量。
