Data hiding for vector quantization images using mixed-base notation and dissimilar patterns without loss of fidelity

NCUIR > Engineering College > Departmant of Mechanical Engineering > journal & Dissertation > Item 987654321/108559

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108559

Title:	Data hiding for vector quantization images using mixed-base notation and dissimilar patterns without loss of fidelity
Authors:	林智揚;Chang, Chin-Chen;Lin, Chih-Yang;Hsieh, Yi-Pei
Contributors:	工學院機械工程學系
Keywords:	Declustering;Mixed base notation;Reversible data embedding
Date:	2012-10-15
Issue Date:	2026-04-23 14:55:05 (UTC+8)
Publisher:	Elsevier Inc.;Elsevier Inc
Abstract:	摘要： Data hiding without loss of fidelity (also called reversible data hiding) allows an original image to be restored completely after extraction of hidden data embedded in a cover image. In this paper, we propose a reversible scheme based on mixed base notation and dissimilar pattern strategies for vector quantization (VQ) compressed images. The dissimilar patterns resulting from the declustering process make the proposed hiding method more efficient. Our experimental results show that the time required for the embedding process in the proposed method is much less than that in the side-matched VQ (SMVQ)-based reversible method. In addition, the use of declustering can reduce many more prediction errors than traditional clustering methods. The results also show that the number of declustered groups and the spatial characteristics of the cover image will affect the embedding capacity and the size of the stego VQ index table. 出版者： Elsevier Inc 出版日期： 2012-10-15 出處： Information sciences, 2012-10, Vol.201, p.70-79 資源來源： ScienceDirect (Elsevier) Journals 版權： 2012 Elsevier Inc. 識別號： ISSN: 0020-0255 識別號： EISSN: 1872-6291 識別號： DOI: 10.1016/j.ins.2011.12.025
Appears in Collections:	[Departmant of Mechanical Engineering ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	17	View/Open

社群 sharing

Loading...