Optimal Gaussian weight predictor and sorting using genetic algorithm for reversible watermarking based on PEE and HS

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dc.contributor.authorPanyindee, Chaiyaporn
dc.contributor.authorPintavirooj, Chuchart
dc.date.accessioned2026-08-06T10:14:07Z
dc.date.available2026-08-06T10:14:07Z
dc.date.issued2016-09-01
dc.description.abstractThis paper introduces a reversible watermarking algorithm that exploits an adaptable predictor and sorting parameter customized for each image and each payload. Our proposed method relies on a well-known prediction-error expansion (PEE) technique. Using small PE values and a harmonious PE sorting parameter greatly decreases image distortion. In order to exploit adaptable tools, Gaussian weight predictor and expanded variance mean (EVM) are used as parameters in this work. A genetic algorithm is also introduced to optimize all parameters and produce the best results possible. Our results show an improvement in image quality when compared with previous conventional works.
dc.identifier.citationIEICE Transactions on Information and Systems, E99D(9), 2306-2319, 2016
dc.identifier.doi10.1587/transinf.2016EDP7030
dc.identifier.issn09168532
dc.identifier.other2-s2.0-84984907219
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/6958
dc.sourceIEICE Transactions on Information and Systems
dc.subjectExpanded variance mean (EVM)
dc.subjectGaussian weight predictor
dc.subjectHistogram shifting (HS)
dc.subjectPrediction-error expansion (PEE)
dc.subjectReversible watermarking
dc.titleOptimal Gaussian weight predictor and sorting using genetic algorithm for reversible watermarking based on PEE and HS
dc.typeArticle

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