Reversible Watermarking Algorithm Using Sorting and Prediction

By Vasiliy Sachnev, Hyoung-Joong Kim, Jeho Nam, Suresh Sundaram, Y. Shi. IEEE transactions on circuits and systems for video technology (Print), 2009

987 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/21854b4de154acec50c2f74c1a0ed8a27b2d3b0a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaolong Li
2. Xinpeng Zhang
3. Pauline Puteaux
4. William Puech
5. Y. Shi
6. Hao-Tian Wu
7. Bin Ma
8. Weiming Zhang

Citations in the corpus

• A Novel Reversible Data Hiding Scheme Based on Two-Dimensional Difference-Histogram Modification (2013, methodology): “On the other hand, since Sachnev et al.'s method modifies each pixel to embed data (recall that in our method, we modify only one pixel in a pixel-pair), its sorting technique still works well while our pixel-pair-selection strategy is no longer effective when our EC approaches its maximum.”
• Reversibility improved data hiding in encrypted images (2014, methodology): “The first step generates a host sequence with small entropy, i.e., the host has a sharp histogram which usually can be realized by using PE combined with the sorting technique [10] or pixel selection [11].”
• Local-Prediction-Based Difference Expansion Reversible Watermarking (2014, methodology): “One gets an average gain in PSNR of 2 dB compared with [16] and of 1.”
• Improved Embedding for Prediction-Based Reversible Watermarking (2011, result): “Let us next compare the results of SGAP3-HS scheme with the ones reported by Sachnev et al. in [23].”
• Reversible data hiding: Advances in the past two decades (2016, methodology): “to generate a more sharply distributed prediction-error histogram [40], [42], [49]–[51], [55], [69]–[77].”
• Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption (2013, methodology): “: The operator here for reserving room before encryption is a standard RDH technique, so the goal of image partition is to construct a smoother area , on which standard RDH algorithms such as [10] , [11] can achieve better performance.”
• Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding (2013, methodology): “EXPERIMENTAL RESULTS AND DISCUSSION In this section, the capacity-distortion performance of the proposed method is evaluated by comparing it with some stateof-the-art works including Luo et al. [25], Sachnev et al. [23], Dragoi and Coltuc [31] and Li et al. [29].”
• General Framework to Histogram-Shifting-Based Reversible Data Hiding (2013, methodology): “[32], RDH algorithm with smaller, or in some cases, no location maps, are very desirable.”
• Efficient Reversible Data Hiding Based on Multiple Histograms Modification (2015, methodology): “Moreover, a sorting technique is used in [40] to record prediction-errors based on the local variance, and a pixel will be priorly embedded if its local variance is small.”
• An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images (2018, methodology): “Methods are based on lossless compression appending, difference expansion [23], [24], histogram shifting [5], [15], [22] or a combination of these schemes [16], [21].”