{"title":"Gradient based prediction for reversible watermarking by difference expansion","authors":"Ioan-Catalin Dragoi, D. Coltuc, I. Caciula","doi":"10.1145/2600918.2600924","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel predictor, EGBSW (Extended Gradient Based Selective Weighting), and investigates its usefulness in difference expansion reversible watermarking. EGBSW is inspired by GBSW, a causal predictor previously used in lossless image compression and known to outperform well-known predictors as the median edge detector (MED) or the gradient-adjusted predictor (GAP). The proposed predictor operates on a larger prediction context than the one of GBSW, namely a rectangular window of 16 pixels located around the pixel to be predicted. Similar to GBSW, the extended predictor computes the gradients on horizontal, vertical and diagonal directions and selects the smallest two gradients. Opposite to the classical predictor, EGSBW uses a set of four simple linear predictors associated with the four principal directions and computes the output value as a weighted sum between the predicted values corresponding to the selected gradients. The reversible watermarking scheme based on EGBSW appears to outperform not only the ones based on GBSW, MED or GAP, but also some recently proposed schemes based on the average on the rhombus context. Experimental results are provided.","PeriodicalId":243756,"journal":{"name":"Information Hiding and Multimedia Security Workshop","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Hiding and Multimedia Security Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2600918.2600924","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
This paper proposes a novel predictor, EGBSW (Extended Gradient Based Selective Weighting), and investigates its usefulness in difference expansion reversible watermarking. EGBSW is inspired by GBSW, a causal predictor previously used in lossless image compression and known to outperform well-known predictors as the median edge detector (MED) or the gradient-adjusted predictor (GAP). The proposed predictor operates on a larger prediction context than the one of GBSW, namely a rectangular window of 16 pixels located around the pixel to be predicted. Similar to GBSW, the extended predictor computes the gradients on horizontal, vertical and diagonal directions and selects the smallest two gradients. Opposite to the classical predictor, EGSBW uses a set of four simple linear predictors associated with the four principal directions and computes the output value as a weighted sum between the predicted values corresponding to the selected gradients. The reversible watermarking scheme based on EGBSW appears to outperform not only the ones based on GBSW, MED or GAP, but also some recently proposed schemes based on the average on the rhombus context. Experimental results are provided.