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

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 d...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2024-05, Vol.12 (9), p.1283
Main Authors: He, Dan, Cai, Zhanchuan, Zhou, Dujuan, Chen, Zhihui
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Channels
color image
Color imagery
Complexity
Computer peripherals industry
Correlation
data security
Digital imaging
Embedding
Histograms
Image enhancement
Image quality
Methods
Modelling
Pixels
Privacy
reversible data hiding
skewed histogram shifting
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12091283