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Abstract: Recently, an efficient reversible lossless data hiding algorithm by Ni et al. was presented. Their fast algorithm can recover the original image without any distortion and its PSNR lower bound is higher than that of all existing reversible data hiding algorithms. Instead of selecting the peak-valley pairs in a greedy way, this paper presents a dynamic programming-based reversible data hiding algorithm to determine the most suitable peak-valley pairs such that the embedding capacity object can be maximized. Based on some artificial map images, experimental results demonstrate that our proposed algorithm has 9% embedding capacity improvement ratio and has the similar image quality performance when compared to Ni et al.’s algorithm although it has some execution-time degradation. For natural images, the embedding capacity of Ni et al.’s algorithm is very close to the maximal embedding capacity obtained by our proposed algorithm. Furthermore, the comparison between our proposed dynamic programming-based algorithm and the reversible data hiding algorithm by Chang et al. is investigated. [Copyright &y& Elsevier]

Abstract: A multiple-choice knapsack problem can be formulated as a discrete nonlinear knapsack problem (DNKP). A powerful method for solving DNKP is the dynamic programming solution approach. The use of this powerful approach however is limited since the growth of the number of decision variables and state variables requires an extensive computer memory storage and computational time. In this paper we developed a hybrid algorithm for improving the computational efficiency of the dynamic programming when it is applied for solving the DNKP. In the hybrid algorithm, three routines of the imbedded state, surrogate constraints, and bounding scheme are incorporated for increasing the efficiency of this solution approach. We then conducted an experimental study for comparing the computational efficiency of the hybrid algorithm with the imbedded state dynamic programming approach. [Copyright &y& Elsevier]