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
zur Publikation
 ( Elsevier B.V.; )
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
  • Online verfügbar (nicht Open Access)
Erscheinungsdatum: 04.2023
ISBN: -
ISSN: 0165-1684
Homepage: https://www.sciencedirect.com/science/article/abs/pii/S0165168422004042?via%3Dihub

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Österreich
   martina.steinbacher@aau.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 1020 - Informatik
Forschungscluster Kein Forschungscluster ausgewählt
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Multimedia Communication

Kooperationen

Organisation Adresse
Southwest Jiaotong University
111 2nd Ring Rd North Section 1
610032 Jinniu District, Chengdu, Sichuan
China
111 2nd Ring Rd North Section 1
CN - 610032  Jinniu District, Chengdu, Sichuan
Southwest Jiaotong University, School of Computing and Artificial Intelligence
111 N 1st Section, 2nd Ring Rd, Sha Xi Mei Shi Yi Tiao Jie, Jinniu Qu
Chengdu
China
111 N 1st Section, 2nd Ring Rd, Sha Xi Mei Shi Yi Tiao Jie, Jinniu Qu
CN  Chengdu

Beiträge der Publikation

Keine verknüpften Publikationen vorhanden