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

Abstract

In this paper, a high-performance error-prediction method based on Multiple Linear Regression (MLR) algorithm is first proposed to improve the performance of Reversible Data Hiding (RDH). The MLR matrix function that indicates the inner correlations 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 fixed parameters predictors through simple arithmetic combination of its surroundings pixel, 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 publicationCloud Computing and Security - 4th International Conference, ICCCS 2018, Revised Selected Papers
EditorsElisa Bertino, Xingming Sun, Zhaoqing Pan
PublisherSpringer Verlag
Pages135-146
Number of pages12
ISBN (Print)9783030000141
DOIs
StatePublished - Jan 1 2018
Event4th International Conference on Cloud Computing and Security, ICCCS 2018 - Haikou, China
Duration: Jun 8 2018Jun 10 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11066 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Cloud Computing and Security, ICCCS 2018
CountryChina
CityHaikou
Period6/8/186/10/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

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

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