2010 10th International Symposium on Communications and Information Technologies 2010
DOI: 10.1109/iscit.2010.5665092
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A reversible data hiding method free from location map and parameter memorization

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Cited by 5 publications
(5 citation statements)
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“…Table 3 and Table 4 show the maximum capacity and corresponding PSNR(dB) in different threshold value k for four images in Fig.9. As shown in Table 5, the simulation result show that the proposed method is perform better than FUIYOSHI et al [10] in image capacity. With the same embedding capacity, the PSNR of our scheme is higher than FUJIYOSHI [10] et al As shown in Fig.10, the dramatic decline caused by the threshold value k, since when k = 0, the maximum capacity in Airplane is 46377, shown as Table 3, in order to get more embedding capacity, the threshold value is changed to k = 1, the maximum capacity is reach to 70453, this is same as Figs.11-13.…”
Section: Resultsmentioning
confidence: 87%
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“…Table 3 and Table 4 show the maximum capacity and corresponding PSNR(dB) in different threshold value k for four images in Fig.9. As shown in Table 5, the simulation result show that the proposed method is perform better than FUIYOSHI et al [10] in image capacity. With the same embedding capacity, the PSNR of our scheme is higher than FUJIYOSHI [10] et al As shown in Fig.10, the dramatic decline caused by the threshold value k, since when k = 0, the maximum capacity in Airplane is 46377, shown as Table 3, in order to get more embedding capacity, the threshold value is changed to k = 1, the maximum capacity is reach to 70453, this is same as Figs.11-13.…”
Section: Resultsmentioning
confidence: 87%
“…The test watermark is a random binary string. We simulate the algorithm proposed by FUJIYOSHI et al [10] to compare the performance with our scheme. The definition of the PSNR (Peak signal-to-noise ratio) is shown as follow:…”
Section: Resultsmentioning
confidence: 99%
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“…19 22 and by Chen et al 23 Finally, a new idea based on pixel prediction is elaborated in a set of recent contributions. Tseng et al 37 study various predictors, while Hu et al 38 improve the location map compressibility, Sachnev et al 39 reduce its size, and Fujiyoshi et al 40 avoid any image-dependent parameter or location map. In Ref.…”
Section: Introductionmentioning
confidence: 99%