Reversible data embedding into images using wavelet techniques and sorting

By Lute Kamstra, Henk J. A. M. Heijmans. IEEE Transactions on Image Processing, 2005

510 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/59df65bec6a91a1eae3a47e04c6922a76906a1bd

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. Bin Yang
3. Weiming Zhang
4. Dinu Coltuc
5. Y. Shi
6. Xinlu Gui
7. Ioan-Catalin Dragoi
8. Tieyong Zeng

Citations in the corpus

• Multilevel reversible data hiding based on histogram modification of difference images (2008, methodology): “[14] improved the DE scheme by using sorting to increase the efficiency of lossless compression.”
• Local-Prediction-Based Difference Expansion Reversible Watermarking (2014, background): “An interesting approach consists of sorting the pixels based on the smoothness of their context in order to embed into pixels with low corresponding differences [15]–[17], etc.”
• Reversible image hiding scheme using predictive coding and histogram shifting (2009, methodology): “Several reversible data hiding schemes have been proposed [12] [13] [14] [15] [16] [17] [18] [19] [20] [21].”
• DE-Based Reversible Data Hiding With Improved Overflow Location Map (2009, methodology): “[10] improved the capacity control and the location map by sorting possible expandable locations.”
• Improved Embedding for Prediction-Based Reversible Watermarking (2011, background): “It should be noticed that the scheme of Sachnev et al. provides lower distortion than the ones of [13], [17], [24], [28], etc.”
• A Novel Reversible Data Hiding Scheme Based on Two-Dimensional Difference-Histogram Modification (2013, methodology): “This method performs well and it is superior to some typical RDH schemes such as [6], [14] and [20].”
• Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform (2007, methodology): “Kamstra and Heijmans improved Fridrich's [3] and Tian's methods [4] by sorting least-significant bits (LSBs) or pairs of pixels to be watermarked with respect to the heuristically obtained values [8].”
• Efficient Reversible Data Hiding Based on Multiple Histograms Modification (2015, methodology): “Afterwards, the expansion technique has been widely investigated and developed, mainly in the aspects of integer-to-integer transformation [18]–[22], location map reduction [23]–[25], and prediction-error expansion (PEE) [26]–[35].”
• A Novel Difference Expansion Transform for Reversible Data Embedding (2008, methodology): “EN1 and EN2 are subsets of the expandable set, EN.”
• Expansion Embedding Techniques for Reversible Watermarking (2007, methodology): “The first algorithm in this category was proposed by Tian [22] and have been extended recently during the course of our work in [1], [17], [18].”
• Reversible data hiding: Advances in the past two decades (2016, methodology): “The first adaptive RDH scheme is the one proposed by Kamstra and Heijmans [53] which is based on DE and sorting.”
• Reversible Watermarking Algorithm Using Sorting and Prediction (2009, methodology): “The combined use of the rhombus prediction scheme, sorting, histogram shift method, and, as result, small size of location map produces relatively superior results compared to existing schemes [7], [9], [14].”
• General Framework to Histogram-Shifting-Based Reversible Data Hiding (2013, methodology): “Kamstra and Heijmans [9] utilized low-pass image to predict expandable locations so that the location map can be remarkably compressed.”