Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number
For the multi-histogram-based reversible watermarking (MHRW) scheme, the performance greatly depends on the multi-histogram construction, which remains a challenge in this field. To generate more desirable multi-histograms, this paper improves the MHRW using the Fuzzy C-Means (FCM) clustering techni...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9139944/ |
| _version_ | 1798001951797936128 |
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| author | Weili Wang Chuntao Wang Junxiang Wang Shan Bian Qiong Huang |
| author_facet | Weili Wang Chuntao Wang Junxiang Wang Shan Bian Qiong Huang |
| author_sort | Weili Wang |
| collection | DOAJ |
| description | For the multi-histogram-based reversible watermarking (MHRW) scheme, the performance greatly depends on the multi-histogram construction, which remains a challenge in this field. To generate more desirable multi-histograms, this paper improves the MHRW using the Fuzzy C-Means (FCM) clustering technique by developing the following approaches: 1)optimize the original feature set, 2)adopt an alternative FCM (AFCM) clustering method, and 3)determine adaptively the optimal clustering number for low embedding rates. These approaches are then integrated to bring about the proposed scheme, i.e., the improved MHRW (IMHRW). Extensive simulations show that the proposed scheme improves the performance of multi-histogram-based reversible watermarking, and it is comparable to or even better than the state of the arts. This thus demonstrates the feasibility and effectiveness of the proposed scheme. |
| first_indexed | 2024-04-11T11:44:26Z |
| format | Article |
| id | doaj.art-677d4f58ced44e4b8f75bd9590dd74d2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:44:26Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-677d4f58ced44e4b8f75bd9590dd74d22022-12-22T04:25:42ZengIEEEIEEE Access2169-35362020-01-01813433413435010.1109/ACCESS.2020.30092759139944Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering NumberWeili Wang0https://orcid.org/0000-0002-9145-5603Chuntao Wang1https://orcid.org/0000-0002-5482-1766Junxiang Wang2https://orcid.org/0000-0003-4371-6212Shan Bian3https://orcid.org/0000-0002-8063-1384Qiong Huang4https://orcid.org/0000-0002-7666-8985College of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaCollege of Mechanical and Electrical Engineering, Jingdezhen Ceramic Institute, Jingdezhen, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaFor the multi-histogram-based reversible watermarking (MHRW) scheme, the performance greatly depends on the multi-histogram construction, which remains a challenge in this field. To generate more desirable multi-histograms, this paper improves the MHRW using the Fuzzy C-Means (FCM) clustering technique by developing the following approaches: 1)optimize the original feature set, 2)adopt an alternative FCM (AFCM) clustering method, and 3)determine adaptively the optimal clustering number for low embedding rates. These approaches are then integrated to bring about the proposed scheme, i.e., the improved MHRW (IMHRW). Extensive simulations show that the proposed scheme improves the performance of multi-histogram-based reversible watermarking, and it is comparable to or even better than the state of the arts. This thus demonstrates the feasibility and effectiveness of the proposed scheme.https://ieeexplore.ieee.org/document/9139944/Reversible watermarkingmultiple histogramsprediction errorfuzzy C-means clusteringlocal feature |
| spellingShingle | Weili Wang Chuntao Wang Junxiang Wang Shan Bian Qiong Huang Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number IEEE Access Reversible watermarking multiple histograms prediction error fuzzy C-means clustering local feature |
| title | Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number |
| title_full | Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number |
| title_fullStr | Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number |
| title_full_unstemmed | Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number |
| title_short | Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number |
| title_sort | improving multi histogram based reversible watermarking using optimized features and adaptive clustering number |
| topic | Reversible watermarking multiple histograms prediction error fuzzy C-means clustering local feature |
| url | https://ieeexplore.ieee.org/document/9139944/ |
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