IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1283-d1381570.html
   My bibliography  Save this article

Inter-Channel Correlation Modeling and Improved Skewed Histogram Shifting for Reversible Data Hiding in Color Images

Author

Listed:
  • Dan He

    (School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
    School of Artificial Intelligence, Dongguan City University, Dongguan 523109, China)

  • Zhanchuan Cai

    (School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Dujuan Zhou

    (School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
    School of Applied Science and Engineering, Beijing Institute of Technology, Zhuhai 519088, China)

  • Zhihui Chen

    (School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China)

Abstract

Reversible data hiding (RDH) is an advanced data protection technology that allows the embedding of additional information into an original digital medium while maintaining its integrity. Color images are typical carriers for information because of their rich data content, making them suitable for data embedding. Compared to grayscale images, color images with their three color channels (RGB) enhance data embedding capabilities while increasing algorithmic complexity. When implementing RDH in color images, researchers often exploit the inter-channel correlation to enhance embedding efficiency and minimize the impact on image visual quality. This paper proposes a novel RDH method for color images based on inter-channel correlation modeling and improved skewed histogram shifting. Initially, we construct an inter-channel correlation model based on the relationship among the RGB channels. Subsequently, an extended method for calculating the local complexity of pixels is proposed. Then, we adaptively select the pixel prediction context and design three types of extreme predictors. The improved skewed histogram shifting method is utilized for data embedding and extraction. Finally, experiments conducted on the USC-SIPI and Kodak datasets validate the superiority of our proposed method in terms of image fidelity.

Suggested Citation

  • Dan He & Zhanchuan Cai & Dujuan Zhou & Zhihui Chen, 2024. "Inter-Channel Correlation Modeling and Improved Skewed Histogram Shifting for Reversible Data Hiding in Color Images," Mathematics, MDPI, vol. 12(9), pages 1-19, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1283-:d:1381570
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1283/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/9/1283/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaoqiang Zhang & Mi Liu & Xiaochang Yang, 2023. "Color Image Encryption Algorithm Based on Cross-Spiral Transformation and Zone Diffusion," Mathematics, MDPI, vol. 11(14), pages 1-28, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Darani, A. Yousefian & Yengejeh, Y. Khedmati & Pakmanesh, H. & Navarro, G., 2024. "Image encryption algorithm based on a new 3D chaotic system using cellular automata," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1283-:d:1381570. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.