Alin Bobeica, Ioan-Catalin Dragoi, I. Caciula, D. Coltuc, F. Albu, Feiran Yang
{"title":"Capacity Control for Prediction Error Expansion based Audio Reversible Data Hiding","authors":"Alin Bobeica, Ioan-Catalin Dragoi, I. Caciula, D. Coltuc, F. Albu, Feiran Yang","doi":"10.1109/ICSTCC.2018.8540672","DOIUrl":null,"url":null,"abstract":"This paper presents an efficient capacity control Algorithm for prediction error expansion based audio reversible data hiding. Current state-of-the-art audio reversible data hiding schemes use a simple capacity control algorithm that was first developed for image reversible data hiding. The performance of this algorithm can be improved by using a simple two threshold based approach. The two threshold approach can be easily integrated into any prediction error expansion based framework. Experimental results are provided for two such frameworks.","PeriodicalId":308427,"journal":{"name":"2018 22nd International Conference on System Theory, Control and Computing (ICSTCC)","volume":"49 S244","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 22nd International Conference on System Theory, Control and Computing (ICSTCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSTCC.2018.8540672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
This paper presents an efficient capacity control Algorithm for prediction error expansion based audio reversible data hiding. Current state-of-the-art audio reversible data hiding schemes use a simple capacity control algorithm that was first developed for image reversible data hiding. The performance of this algorithm can be improved by using a simple two threshold based approach. The two threshold approach can be easily integrated into any prediction error expansion based framework. Experimental results are provided for two such frameworks.