Reversible Data Hiding with a New Local Contrast Enhancement Approach
<p>Histogram embedding in histogram shifting scheme; (<b>a</b>) original histogram; (<b>b</b>) shifted histogram; (<b>c</b>) histogram with concealed data.</p> "> Figure 2
<p>Representation of the (<b>a</b>) original, (<b>b</b>) preprocessed, (<b>c</b>) final histograms. (<b>d</b>) Original, and (<b>e</b>) contrasted images [<a href="#B25-mathematics-10-00841" class="html-bibr">25</a>].</p> "> Figure 3
<p>Embedding and removal stages based on MRLHS.</p> "> Figure 4
<p>Detailed MRLHS process: (<b>a</b>) embedding and (<b>b</b>) removal.</p> "> Figure 5
<p>(<b>a</b>) Original image, (<b>b</b>) enhanced image without block shifting, and (<b>c</b>) enhanced image with block shifting.</p> "> Figure 6
<p>Proposed embedding procedure with a minimum bin: (<b>a</b>) original histogram; (<b>b</b>) shifted histogram; (<b>c</b>) histogram with concealed data.</p> "> Figure 7
<p>(<b>a</b>) Example of a histogram; (<b>b</b>) one range selected with capacity equal to 14 bits; (<b>c</b>) three ranges selected with total capacity equal to 20 bits; (<b>d</b>) Histogram with concealed information in the range of (<b>b</b>); (<b>e</b>) Histogram with concealed information in the three ranges of (<b>c</b>).</p> "> Figure 8
<p>Optimization process based on iterative updating of vectors <math display="inline"><semantics> <mi mathvariant="bold-italic">a</mi> </semantics></math> and <math display="inline"><semantics> <mi mathvariant="bold-italic">b</mi> </semantics></math>.</p> "> Figure 9
<p>PSNR visual quality according to hiding rate.</p> "> Figure 10
<p>Original images: (<b>a</b>) F-16; (<b>b</b>) Lena; (<b>c</b>) Baboon with a selected block (red square); (<b>d</b>–<b>f</b>) original histogram (black line) and enhanced histogram (red line) of the selected block.</p> "> Figure 11
<p>Assessment metrics with different hiding rates for F-16, Lena, and Baboon images: RCE (<b>a</b>–<b>c</b>); BRISQUE (<b>d</b>–<b>f</b>); PSNR (<b>g</b>–<b>i</b>); SSIM (<b>j</b>–<b>l</b>).</p> "> Figure 12
<p>(<b>a</b>,<b>d</b>,<b>g</b>) Original images; (<b>b</b>,<b>e</b>,<b>h</b>) enhanced images with internal removal sequence <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </semantics></math> embedded; (<b>c</b>,<b>f</b>,<b>i</b>) enhanced images with internal removal sequence <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </semantics></math> and binary string <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>S</mi> </mrow> </semantics></math> embedded.</p> "> Figure 13
<p>Embedding time according to (<b>a</b>) hiding rate with <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>C</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, (<b>b</b>) number of blocks (<math display="inline"><semantics> <mrow> <mi>F</mi> <mo>×</mo> <mi>C</mi> </mrow> </semantics></math>) for a hiding rate equal to 0.2 bpp, and (<b>c</b>) image size with a hiding rate of 0.5 bpp and <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>C</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>.</p> "> Figure 14
<p>Relation between embedding time and removal time using <a href="#mathematics-10-00841-f013" class="html-fig">Figure 13</a>a information.</p> "> Figure 15
<p>Test images used in the experimental results of <a href="#mathematics-10-00841-t007" class="html-table">Table 7</a> and <a href="#mathematics-10-00841-t008" class="html-table">Table 8</a>.</p> "> Figure 16
<p>Two original images of USC-SIP dataset (<b>a</b>) Baboon and (<b>e</b>) F-16. Enhanced images obtained by algorithms in (<b>b</b>,<b>f</b>) [<a href="#B21-mathematics-10-00841" class="html-bibr">21</a>], (<b>c</b>,<b>g</b>) [<a href="#B31-mathematics-10-00841" class="html-bibr">31</a>], and (<b>d</b>,<b>h</b>) the present proposal.</p> "> Figure 17
<p>Two original images of USC-SIP dataset: (<b>a</b>) parrots and (<b>e</b>) house. Enhanced images obtained by algorithms in (<b>b</b>,<b>f</b>) [<a href="#B21-mathematics-10-00841" class="html-bibr">21</a>], (<b>c</b>,<b>g</b>) [<a href="#B31-mathematics-10-00841" class="html-bibr">31</a>], and (<b>d</b>,<b>h</b>) the present proposal.</p> "> Figure 17 Cont.
<p>Two original images of USC-SIP dataset: (<b>a</b>) parrots and (<b>e</b>) house. Enhanced images obtained by algorithms in (<b>b</b>,<b>f</b>) [<a href="#B21-mathematics-10-00841" class="html-bibr">21</a>], (<b>c</b>,<b>g</b>) [<a href="#B31-mathematics-10-00841" class="html-bibr">31</a>], and (<b>d</b>,<b>h</b>) the present proposal.</p> "> Figure 18
<p>Zoomed parts of some original images (<b>left column</b>), enhanced images obtained by algorithm in [<a href="#B31-mathematics-10-00841" class="html-bibr">31</a>] (<b>centered column</b>), and by our proposal (<b>right column</b>).</p> ">
Abstract
:1. Introduction
- Firstly, since the merged bins are not adjacent, the change in pixel values may cause some visual artifacts in the image;
- Secondly, the position of the merged pixels must be saved to ensure reversibility; thus, a location map is generated and hidden into the histogram. Since the location map is the size of the image, it must be compressed; nevertheless, the payload capacity is reduced by the additional hidden information.
2. Background
2.1. Embedding Process of the Conventional Histogram Shifting
2.2. Reversibility Process of the Conventional Histogram Shifting
2.3. Reversible Data Hiding with Contrast Enhancement
3. Proposed RDH-CE Based on MRLHS
- Embed the binary string into the image using the MRLHS embedding process. This produces an enhanced internal image and the removal information ;
- Generate internal removal sequence from the removal information ;
- Embed internal removal sequence into the image . This produces the enhanced image and the new removal information ;
- Generate external removal sequence with the new removal information .
- Read the information of the external removal sequence to obtain removal information ;
- Extract the internal removal sequence and obtain an internally enhanced image ;
- Extract the information of the internal removal sequence to obtain new removal information ;
- Extract the binary vector and the image , which experimental results demonstrate are equal to the binary string and image , respectively.
Algorithm 1: Embedding stage (Figure 3) |
Inputs: Image and binary string |
Outputs: Enhanced image and removal sequence k |
MRLHS embedding for BS (Figure 4a): |
Initialize enhanced image and level |
while is not totally embedded |
Upgrade |
Divide image into blocks (with block shifting) |
Calculate histogram of each block |
Select ranges of each histogram (Section 3.2) |
Embed a segment of into each range of Ji (Section 3.1) |
Identify data for removal information : (a) (b) peak bins and minimum bins (c) (d) length |
end while |
Generate internal removal sequence ki from RI (Section 3.3.1) |
MRLHS embedding for (Figure 4a): |
Initialize enhanced image and level |
while is not totally embedded |
Upgrade |
Divide image into blocks (with block shifting) |
Calculate histogram of each block |
Select ranges of each histogram (Section 3.2) |
Embed a segment of into each range of J (Section 3.1) |
Identify data for removal information : (a) (b) peak bins and minimum bins (c) (d) length |
end while |
Generate internal removal sequence from (Section 3.3.1) |
Algorithm 2: Removal stage (Figure 3) |
Inputs: Enhanced image and removal sequence |
Outputs: Recovered image and recovered binary string BS |
Read the removal information from removal sequence k (Section 3.3.2) |
MRLHS removal for (Figure 4b): |
Initialize recovered image |
while is not totally removed () |
Divide image into blocks (with block shifting) |
Calculate histogram of each block |
Remove a segment of from each range defined with corresponding peak bin and min-bin (Section 3.1.2) |
Upgrade |
end while |
Read the removal information RI from removal sequence (Section 3.3.2) |
MRLHS removal for (Figure 4b): |
Initialize recovered image |
while is not totally removed () |
Divide image into blocks (with block shifting) |
Calculate histogram of each block |
Remove a segment of from each range defined with corresponding peak bin a and min bin (Section 3.1.2) |
Upgrade |
end while |
Concatenate segments to recover (Section 3.3.1) |
3.1. Embedding Process of the Conventional Histogram Shifting
3.1.1. HS-MB Embedding
3.1.2. HS-MB Removal
3.2. Selection of Optimal Set of Ranges
Optimization Process
3.3. Generation and Reading of Removal Sequences
3.3.1. Generation of Internal and User Removal Sequence
- (): Predefined flag to separate main parameters;
- (): Flags to separate the peak and the minimum bins;
- (), (): Block division in rows and columns;
- (): Number of embedding levels;
- (): Length of the binary string ;
- (): All peak bins;
- (): All minimum bins;
- (): Total bits to represent the number of ranges per block;
- (): Total bits to represent the peak bins;
- (): Total bits to represent the minimum bins.
3.3.2. Reading of Internal and User Removal Sequence
4. Experimental Results
4.1. Assessment Metrics to Contrast-Enhancement and Visual Quality
4.2. Parameters Settings
4.3. Performance on Individual Images
4.4. Computational Complexity (Speed)
4.5. Performance Comparison
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Shi, Y.Q.; Li, X.; Zhang, X.; Wu, H.T.; Ma, B. Reversible Data Hiding: Advances in the Past Two Decades. IEEE Access 2016, 4, 3210–3237. [Google Scholar] [CrossRef]
- Fridrich, J.; Goljan, M.; Du, R. Invertible Authentication. Proc. SPIE-Int. Soc. Opt. Eng. 2001, 4314, 197–208. [Google Scholar] [CrossRef]
- Fridrich, J.; Goljan, M.; Du, R. Lossless Data Embedding-New Paradigm in Digital Watermarking. EURASIP J. Appl. Signal Process. 2002, 2002, 185–196. [Google Scholar] [CrossRef] [Green Version]
- Celik, M.U.; Sharma, G.; Tekalp, A.M.; Saber, E. Lossless Generalized-LSB Data Embedding. IEEE Trans. Image Process. 2005, 14, 253–266. [Google Scholar] [CrossRef] [PubMed]
- Tian, J. Reversible Data Embedding Using a Difference Expansion. IEEE Trans. Circuits Syst. Video Technol. 2003, 13, 890–896. [Google Scholar] [CrossRef] [Green Version]
- Tian, J. Wavelet-Based Reversible Watermarking for Authentication. Secur. Watermarking Multimed. Contents IV 2002, 4675, 679–690. [Google Scholar] [CrossRef]
- Thodi, D.M.; Rodríguez, J.J. Prediction-Error Based Reversible Watermarking. Proc.-Int. Conf. Image Process. ICIP 2004, 3, 1549–1552. [Google Scholar] [CrossRef]
- Thodi, D.M.; Rodríguez, J.J. Expansion Embedding Techniques for Reversible Watermarking. IEEE Trans. Image Process. 2007, 16, 721–730. [Google Scholar] [CrossRef]
- Ni, Z.; Shi, Y.Q.; Ansari, N.; Su, W. Reversible Data Hiding. IEEE Trans. Circuits Syst. Video Technol. 2006, 16, 354–361. [Google Scholar] [CrossRef]
- Coatrieux, G.; Pan, W.; Cuppens-Boulahia, N.; Cuppens, F.; Roux, C. Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting. IEEE Trans. Inf. Forensics Secur. 2013, 8, 111–120. [Google Scholar] [CrossRef]
- Lee, S.K.; Suh, Y.H.; Ho, Y.S. Reversible Image Authentication Based on Watermarking. In Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, Toronto, ON, Canada, 9–12 July 2006; pp. 1321–1324. [Google Scholar] [CrossRef]
- Li, X.; Zhang, W.; Gui, X.; Yang, B. Efficient Reversible Data Hiding Based on Multiple Histograms Modification. IEEE Trans. Inf. Forensics Secur. 2015, 10, 2016–2027. [Google Scholar] [CrossRef]
- Ou, B.; Li, X.; Zhao, Y.; Ni, R. Reversible Data Hiding Using Invariant Pixel-Value-Ordering and Prediction-Error Expansion. Signal Process. Image Commun. 2014, 29, 760–772. [Google Scholar] [CrossRef]
- Sachnev, V.; Kim, H.J.; Nam, J.; Suresh, S.; Shi, Y.Q. Reversible Watermarking Algorithm Using Sorting and Prediction. IEEE Trans. Circuits Syst. Video Technol. 2009, 19, 989–999. [Google Scholar] [CrossRef]
- Fallahpour, M.; Sedaaghi, M.H. High Capacity Lossless Data Hiding Based on Histogram Modification. IEICE Electron. Express 2007, 4, 205–210. [Google Scholar] [CrossRef] [Green Version]
- Fallahpour, M. Reversible Image Data Hiding Based on Gradient Adjusted Prediction. IEICE Electron. Express 2008, 5, 870–876. [Google Scholar] [CrossRef] [Green Version]
- Hu, Y.; Lee, H.K.; Li, J. DE-Based Reversible Data Hiding with Improved Overflow Location Map. IEEE Trans. Circuits Syst. Video Technol. 2009, 19, 250–260. [Google Scholar] [CrossRef]
- Hong, W.; Chen, T.S.; Shiu, C.W. Reversible Data Hiding for High Quality Images Using Modification of Prediction Errors. J. Syst. Softw. 2009, 82, 1833–1842. [Google Scholar] [CrossRef]
- Hamad, S.; Khalifa, A.; Elhadad, A. A Blind High-Capacity Wavelet-Based Steganography Technique for Hiding Images into other Images. Adv. Electr. Comput. Eng. 2015, 14, 35–42. [Google Scholar] [CrossRef]
- Elhadad, A.; Ghareeb, A.; Abbas, S. A Blind and High-Capacity Data Hiding of DICOM Medical Images Based on Fuzzification Concepts. Alex. Eng. J. Lett. 2021, 60, 2471–2482. [Google Scholar] [CrossRef]
- Wu, H.T.; Dugelay, J.L.; Shi, Y.Q. Reversible Image Data Hiding with Contrast Enhancement. IEEE Signal Process. Lett. 2015, 22, 81–85. [Google Scholar] [CrossRef]
- Stark, J.A. Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization. IEEE Trans. Image Process. 2000, 9, 889–896. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cao, G.; Tian, H.; Yu, L.; Huang, X.; Wang, Y. Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling. IET Image Process. 2018, 12, 447–452. [Google Scholar] [CrossRef] [Green Version]
- Wu, H.T.; Huang, J.; Shi, Y.Q. A Reversible Data Hiding Method with Contrast Enhancement for Medical Images. J. Vis. Commun. Image Represent. 2015, 31, 146–153. [Google Scholar] [CrossRef]
- Kim, S.; Lussi, R.; Qu, X.; Kim, H.J. Automatic Contrast Enhancement Using Reversible Data Hiding. In Proceedings of the 2015 IEEE International Workshop on Information Forensics and Security (WIFS), Rome, Italy, 16–19 November 2015; pp. 1–5. [Google Scholar] [CrossRef]
- Gao, G.; Shi, Y.Q. Reversible Data Hiding Using Controlled Contrast Enhancement and Integer Wavelet Transform. IEEE Signal Process. Lett. 2015, 22, 2078–2082. [Google Scholar] [CrossRef]
- Moorthy, A.K.; Bovik, A.C. Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality. IEEE Trans. Image Process. 2011, 20, 3350–3364. [Google Scholar] [CrossRef]
- Chen, H.; Ni, J.; Hong, W.; Chen, T.S. Reversible Data Hiding with Contrast Enhancement Using Adaptive Histogram Shifting and Pixel Value Ordering. Signal Process. Image Commun. 2016, 46, 1–16. [Google Scholar] [CrossRef]
- Wu, H.T.; Tang, S.; Huang, J.; Shi, Y.Q. A Novel Reversible Data Hiding Method with Image Contrast Enhancement. Signal Process. Image Commun. 2018, 62, 64–73. [Google Scholar] [CrossRef]
- Ying, Q.; Qian, Z.; Zhang, X.; Ye, D. Reversible Data Hiding with Image Enhancement Using Histogram Shifting. IEEE Access 2019, 7, 46506–46521. [Google Scholar] [CrossRef]
- Wu, H.T.; Mai, W.; Meng, S.; Cheung, Y.M.; Tang, S. Reversible Data Hiding with Image Contrast Enhancement Based on Two-Dimensional Histogram Modification. IEEE Access 2019, 7, 83332–83342. [Google Scholar] [CrossRef]
- The USC-Sipi Image Database. Available online: http://sipi.usc.edu/database/ (accessed on 20 November 2021).
- Kodak Lossless True Color Image Suite. Available online: http://www.r0k.us/graphics/kodak/ (accessed on 20 November 2021).
- BOWS-2. Available online: http://bows2.gipsa-lab.inpg.fr (accessed on 10 January 2019).
- Mittal, A.; Moorthy, A.K.; Bovik, A.C. No-Reference Image Quality Assessment in the Spatial Domain. IEEE Trans. Image Process. 2012, 21, 4695–4708. [Google Scholar] [CrossRef]
- Wang, Z.; Bovik, A.C.; Sheikh, H.R.; Simoncelli, E.P. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. Image Process. 2004, 13, 600–612. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Case | Condition 1 | Condition 2 | Condition 3 | ||
---|---|---|---|---|---|
1 | |||||
2 | |||||
3 | |||||
4 | |||||
5 | |||||
6 | |||||
7 | |||||
8 | |||||
9 | |||||
10 | |||||
(delimiter flag) | 1011001110001111 | 1011001110001111 |
(stop value) | 0.05 | 0.05 |
1, 2, 4, 8 | 1 |
Inputs | 2 | 4 | 8 | |
1 | ||||
length (0.8 bpp) (Bytes) | 26214 | |||
Outputs | length (Bytes) | 795 | 1953 | 4565 |
length (Bytes) | 36 | 51 | 75 | |
Embedding time (s) | 69 | 133 | 288 | |
Embedding levels for | 34 | 22 | 14 | |
Embedding levels for | 2 | 4 | 8 | |
RCE | 0.580 | 0.549 | 0.539 | |
PSNR (dB) | 20.96 | 25.66 | 29.49 | |
SSIM | 0.817 | 0.828 | 0.823 |
S | Hiding Rate | Method | RCE | REE | RMBE | BRSIQUE | SSIM | PSNR (dB) |
---|---|---|---|---|---|---|---|---|
20 | 0.458 | [21] | 0.548 | 0.526 | 0.989 | 23.54 | 0.920 | 25.05 |
0.451 | [24] | 0.547 | 0.525 | 0.989 | 23.41 | 0.927 | 25.09 | |
0.482 | [25] | 0.537 | 0.525 | 0.977 | 22.19 | 0.943 | 26.43 | |
0.452 | [29] | 0.543 | 0.526 | 0.987 | 22.74 | 0.936 | 25.54 | |
0.447 | [31] | 0.545 | 0.530 | 0.990 | 22.64 | 0.932 | 25.45 | |
0.450 | Proposed | 0.518 | 0.515 | 0.997 | 30.13 | 0.923 | 31.99 | |
30 | 0.607 | [21] | 0.567 | 0.533 | 0.983 | 27.89 | 0.852 | 21.72 |
0.590 | [24] | 0.565 | 0.532 | 0.983 | 27.00 | 0.860 | 22.01 | |
0.582 | [29] | 0.555 | 0.533 | 0.980 | 23.48 | 0.903 | 23.27 | |
0.581 | [31] | 0.561 | 0.538 | 0.985 | 23.47 | 0.893 | 22.94 | |
0.600 | Proposed | 0.527 | 0.521 | 0.995 | 30.16 | 0.878 | 28.85 | |
50 | 0.865 | [21] | 0.581 | 0.537 | 0.966 | 44.38 | 0.647 | 17.28 |
0.835 | [24] | 0.571 | 0.535 | 0.952 | 40.56 | 0.675 | 17.44 | |
0.850 | Proposed | 0.555 | 0.532 | 0.990 | 32.09 | 0.755 | 23.68 | |
0.676 | [29] | 0.572 | 0.538 | 0.967 | 24.30 | 0.853 | 20.76 | |
0.700 | [31] | 0.583 | 0.546 | 0.962 | 24.69 | 0.822 | 19.69 | |
0.675 | Proposed | 0.534 | 0.524 | 0.994 | 30.57 | 0.840 | 27.18 |
S | Hiding Rate | Method | RCE | REE | RMBE | BRSIQUE | SSIM | PSNR (dB) |
---|---|---|---|---|---|---|---|---|
20 | 0.511 | [21] | 0.544 | 0.528 | 0.985 | 16.65 | 0.902 | 24.89 |
0.496 | [24] | 0.539 | 0.529 | 0.984 | 15.42 | 0.914 | 24.70 | |
0.563 | [25] | 0.541 | 0.532 | 0.966 | 14.01 | 0.915 | 23.37 | |
0.476 | [29] | 0.537 | 0.528 | 0.982 | 14.00 | 0.928 | 25.02 | |
0.518 | [31] | 0.539 | 0.534 | 0.982 | 13.79 | 0.921 | 24.85 | |
0.500 | Proposed | 0.513 | 0.515 | 0.997 | 25.72 | 0.947 | 34.43 | |
30 | 0.695 | [21] | 0.560 | 0.535 | 0.976 | 20.57 | 0.831 | 21.34 |
0.655 | [24] | 0.555 | 0.535 | 0.975 | 19.84 | 0.843 | 21.53 | |
0.564 | [29] | 0.550 | 0.533 | 0.970 | 15.08 | 0.890 | 22.27 | |
0.639 | [31] | 0.555 | 0.541 | 0.974 | 15.59 | 0.871 | 21.74 | |
0.638 | Proposed | 0.520 | 0.520 | 0.996 | 25.65 | 0.913 | 31.84 | |
50 | 0.847 | [21] | 0.581 | 0.537 | 0.966 | 44.38 | 0.647 | 17.28 |
0.800 | [24] | 0.571 | 0.535 | 0.952 | 40.56 | 0.675 | 17.44 | |
0.817 | Proposed | 0.550 | 0.530 | 0.991 | 31.80 | 0.775 | 24.400 | |
0.603 | [29] | 0.572 | 0.538 | 0.967 | 24.30 | 0.853 | 20.76 | |
0.715 | [31] | 0.583 | 0.546 | 0.962 | 24.69 | 0.822 | 19.69 | |
0.638 | Proposed | 0.520 | 0.520 | 0.996 | 25.65 | 0.913 | 31.84 |
S Value | Hiding Rate | Method | RCE | REE | RMBE | BRSIQUE | SSIM | PSNR (dB) |
---|---|---|---|---|---|---|---|---|
20 | 0.511 | [21] | 0.544 | 0.528 | 0.985 | 16.65 | 0.902 | 24.89 |
0.496 | [24] | 0.539 | 0.529 | 0.984 | 15.42 | 0.914 | 24.70 | |
0.563 | [25] | 0.541 | 0.532 | 0.966 | 14.01 | 0.915 | 23.37 | |
0.476 | [29] | 0.537 | 0.528 | 0.982 | 14.00 | 0.928 | 25.02 | |
0.518 | [31] | 0.539 | 0.534 | 0.982 | 13.79 | 0.921 | 24.85 | |
0.510 | Proposed | 0.547 | 0.533 | 0.987 | 9.75 | 0.930 | 25.74 | |
30 | 0.695 | [20] | 0.560 | 0.535 | 0.976 | 20.57 | 0.831 | 21.34 |
0.655 | [24] | 0.555 | 0.535 | 0.975 | 19.84 | 0.843 | 21.53 | |
0.564 | [29] | 0.550 | 0.533 | 0.970 | 15.08 | 0.890 | 22.27 | |
0.639 | [31] | 0.555 | 0.541 | 0.974 | 15.59 | 0.871 | 21.74 | |
0.638 | Proposed | 0.563 | 0.541 | 0.975 | 12.21 | 0.890 | 22.97 |
Hiding Rate | Method | RCE | REE | RMBE | SSIM | PSNR |
---|---|---|---|---|---|---|
0.708 | [30] | 0.553 | 0.536 | 0.958 | 0.871 | 22.99 |
0.670 | [21] | 0.592 | 0.528 | 0.960 | 0.828 | 22.58 |
0.699 | [26] | 0.555 | 0.532 | 0.968 | 0.858 | 23.34 |
0.691 | [28] | 0.557 | 0.530 | 0.968 | 0.860 | 22.53 |
0.691 | Proposed | 0.518 | 0.527 | 0.997 | 0.915 | 32.40 |
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Fragoso-Navarro, E.; Cedillo-Hernandez, M.; Garcia-Ugalde, F.; Morelos-Zaragoza, R. Reversible Data Hiding with a New Local Contrast Enhancement Approach. Mathematics 2022, 10, 841. https://doi.org/10.3390/math10050841
Fragoso-Navarro E, Cedillo-Hernandez M, Garcia-Ugalde F, Morelos-Zaragoza R. Reversible Data Hiding with a New Local Contrast Enhancement Approach. Mathematics. 2022; 10(5):841. https://doi.org/10.3390/math10050841
Chicago/Turabian StyleFragoso-Navarro, Eduardo, Manuel Cedillo-Hernandez, Francisco Garcia-Ugalde, and Robert Morelos-Zaragoza. 2022. "Reversible Data Hiding with a New Local Contrast Enhancement Approach" Mathematics 10, no. 5: 841. https://doi.org/10.3390/math10050841
APA StyleFragoso-Navarro, E., Cedillo-Hernandez, M., Garcia-Ugalde, F., & Morelos-Zaragoza, R. (2022). Reversible Data Hiding with a New Local Contrast Enhancement Approach. Mathematics, 10(5), 841. https://doi.org/10.3390/math10050841