Reversible data hiding using least square predictor via the LASSO

Hee Joon Hwang; Sung Hwan Kim; Hyoung Joong Kim

doi:10.1186/s13640-016-0144-3

Reversible data hiding using least square predictor via the LASSO

Hee Joon Hwang, Sung Hwan Kim, Hyoung Joong Kim

Graduate School of Information Study

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Reversible watermarking is a kind of digital watermarking which is able to recover the original image exactly as well as extracting hidden message. Many algorithms have aimed at lower image distortion in higher embedding capacity. In the reversible data hiding, the role of efficient predictors is crucial. Recently, adaptive predictors using least square approach have been proposed to overcome the limitation of the fixed predictors. This paper proposes a novel reversible data hiding algorithm using least square predictor via least absolute shrinkage and selection operator (LASSO). This predictor is dynamic in nature rather than fixed. Experimental results show that the proposed method outperforms the previous methods including some algorithms which are based on the least square predictors.

Original language	English
Article number	42
Journal	Eurasip Journal on Image and Video Processing
Volume	2016
Issue number	1
DOIs	https://doi.org/10.1186/s13640-016-0144-3
Publication status	Published - 2016 Dec 1

ASJC Scopus subject areas

Signal Processing
Information Systems
Electrical and Electronic Engineering

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abstract = "Reversible watermarking is a kind of digital watermarking which is able to recover the original image exactly as well as extracting hidden message. Many algorithms have aimed at lower image distortion in higher embedding capacity. In the reversible data hiding, the role of efficient predictors is crucial. Recently, adaptive predictors using least square approach have been proposed to overcome the limitation of the fixed predictors. This paper proposes a novel reversible data hiding algorithm using least square predictor via least absolute shrinkage and selection operator (LASSO). This predictor is dynamic in nature rather than fixed. Experimental results show that the proposed method outperforms the previous methods including some algorithms which are based on the least square predictors.",

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