Fast Expansion-Bins-Determination for Multiple Histograms Modification Based Reversible Data Hiding

IEEE SIGNAL PROCESSING LETTERS(2022)

引用 10|浏览26
暂无评分
摘要
Reversible data hiding (RDH) is a research hotspot nowadays. By RDH, after data extraction, the cover image can be restored without information loss. Among numerous existing RDH techniques, multiple histograms modification (MHM) is a general reversible embedding framework, and it is experimentally verified better than the traditional single histogram based methods. However, the expansion-bins-determination process for MHM is conducted through naive exhaustive search, which is time consuming. Based on this consideration, a fast expansion-bins-determination method for MHM is proposed in this paper. Specifically, to determine the optimal expansion bins, instead of solving the optimization problem of discrete variables, we consider a general form of this problem with differentiable objective function and real variables, so that advanced analysis tools such as Lagrange multiplier can be utilized. By the proposed approach, compared with the original MHM, the expansion bins can be determined quickly with only a tiny performance loss, and thus the practicality of MHM is improved.
更多
查看译文
关键词
Histograms,Complexity theory,Optimization,Image restoration,Data mining,Nickel,Linear programming,Reversible data hiding,expansion and shifting,multiple histograms modification,fast parameters determination
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要