A multiple linear regression based high-performance error prediction method for reversible data hiding

Bin Ma, Xiaoyu Wang, Bing Li, Yunqing Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, a high-performance error-prediction method based on multiple linear regression (MLR) algorithm is proposed to improve the performance of reversible data hiding (RDH). The MLR matrix function indicates the inner correlation between the pixels and its neighbors is established adaptively according to the consistency of pixels in local area of a natural image, and thus the object pixel is predicted accurately with the achieved MLR function that satisfies the consistency of the neighboring pixels. Compared with conventional methods that only predict the object pixel with simple arithmetic combination of its surroundings pixel, the experimental results show that the proposed method can provide a sparser prediction-error image for data embedding, and thus improves the performance of RDH more effectively than those state-of-the-art error prediction algorithms.

Original languageEnglish (US)
Title of host publicationSecurity and Privacy in Communication Networks - 14th International Conference, SecureComm 2018, Proceedings
EditorsBing Chang, Yingjiu Li, Raheem Beyah, Sencun Zhu
PublisherSpringer Verlag
Pages441-452
Number of pages12
ISBN (Print)9783030017033
DOIs
StatePublished - Jan 1 2018
Event14th International EAI Conference on Security and Privacy in Communication Networks, SecureComm 2018 - Singapore, Singapore
Duration: Aug 8 2018Aug 10 2018

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume255
ISSN (Print)1867-8211

Other

Other14th International EAI Conference on Security and Privacy in Communication Networks, SecureComm 2018
CountrySingapore
CitySingapore
Period8/8/188/10/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Embedded capacity
  • Multiple linear regression
  • Prediction error
  • Reversible data hiding

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