Publikation: Reversible data hiding for color images...
Stammdaten
Titel: | Reversible data hiding for color images based on pixel value order of overall process channel |
Untertitel: | |
Kurzfassung: | Color image Reversible Data Hiding (RDH) is getting more and more important since the number of its applications is steadily growing. This paper proposes an efficient color image RDH scheme based on pixel value ordering (PVO), in which the channel correlation is fully utilized to improve the embedding performance. In the proposed method, the channel correlation is used in the overall process of data embedding including prediction stage, block selection and capacity allocation. In the prediction stage, since the pixel values in the co-located blocks in different channels are monotonically consistent, the large pixel values are collected preferentially by pre-sorting the intra-block pixels. This can effectively improve the embedding capacity of RDH based on PVO. In the block selection stage, the description accuracy of block complexity value is improved by exploiting the texture similarity between the channels. The smoothing the block is then preferentially used to reduce invalid shifts. To achieve low complexity and high accuracy in capacity allocation, the proportion of the expanded prediction error to the total expanded prediction error in each channel is calculated during the capacity allocation process. The experimental results show that the proposed scheme achieves significant superiority in fidelity over a series of state-of-the-art schemes. For example, the PSNR of the Lena image reaches 62.43 dB, which is a 0.16 dB gain compared to the best results in the literature with a 20,000bits embedding capacity. |
Schlagworte: | Reversible data hiding, Color image, Pixel value ordering, Channel correlation |
Publikationstyp: | Beitrag in Zeitschrift (Autorenschaft) |
Erscheinungsdatum: | 19.11.2022 (Online) |
Erschienen in: |
Signal Processing
Signal Processing
(
Elsevier B.V.;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | 205 |
Heftnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | - |
Gesamtseitenanzahl: | 108865 S. |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 19.11.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1016/j.sigpro.2022.108865 |
Homepage: | https://www.sciencedirect.com/science/article/abs/pii/S0165168422004042?via%3Dihub |
Open Access |
|
Erscheinungsdatum: | 04.2023 |
ISBN: | - |
ISSN: | 0165-1684 |
Homepage: | https://www.sciencedirect.com/science/article/abs/pii/S0165168422004042?via%3Dihub |
AutorInnen
Ningxiong Mao (extern) |
Hongjie He (extern) |
Fan Chen (extern) |
Lingfeng Qu (extern) |
Hadi Amirpourazarian (intern) |
Christian Timmerer (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
Sachgebiete | |
Forschungscluster | Kein Forschungscluster ausgewählt |
Zitationsindex |
Informationen zum Zitationsindex: Master Journal List
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Peer Reviewed |
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Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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Arbeitsgruppen |
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Kooperationen
Organisation | Adresse | ||
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Southwest Jiaotong University
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CN - 610032 Jinniu District, Chengdu, Sichuan |
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Southwest Jiaotong University, School of Computing and Artificial Intelligence
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CN Chengdu |
Forschungsaktivitäten
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Veranstaltungen: | Keine verknüpften Veranstaltung vorhanden |
Vorträge: | Keine verknüpften Vorträge vorhanden |