Hight capacity reversible data hiding by prediction algorithm for image 沈詠睿、張世旭
E-mail: [email protected]
ABSTRACT
In the image data hiding, reversible data hiding can extraction of information in the image and can restore the original image, which can guarantee integrality and exactness of the embed data. In reference, researcher proposed data hiding based on Histogram Modification, which embed the information on same absolute difference pixel value, hiding capacity to have relations with the number of same absolute difference pixel value, when the same difference pixel value presents the number of times to be more hiding capacity is bigger. In this paper, we proposed a hiding scheme with prediction method to improve the accuracy of the predicted values, which makes the difference highly centralized in ‘0’ or ‘1’ , that can raise the number of same absolute difference pixel value, then raise the higher capacity. Input cover image’s pixel value x minus the predicted value generate the difference value, and statistical histogram. In the histogram identify the highest wave peak is p. Stego image gray values is y, data could be embedded in the pixel point with p. if the difference value greater than the pixel point with p, then y to be equal to x modified a constant. if the difference value to be smaller than the pixel point with p, then y to be equal to x. if the pixel after change greater than or equal 255 or the pixel after change smaller than or equal 0, make the gray value is 255 or 0,and additional record the gray change vaule . When using the JPEG-LS predictor ,the average capacity is 562224 bits and the average PSNR of the cover image is 43.31dB. In the same average capacity,the proposed method have higher image quality than other reversible data hiding scheme.
Keywords : Reversible data hiding、JPEG-LS、Histogram、Difference value、CALIC Table of Contents
封面內頁 簽名頁 授權書iii 中文摘要iv ABSTRACTv 誌謝vi 目錄vii 圖目錄ix 表目錄x 第一章 緒論1 1.1 研究動機1 1.2 文獻探 討2 1.3 論文架構3 第二章相關文獻介紹4 2.1差值擴張法[5]4 2.2差值直方圖修改法[9]7 2.3灰階值直方圖修改法[11]10 2.4 JPEG-LS預測法11 第三章研究方法15 3.1 Two-pass嵌入演算法16 3.2 One-Pass嵌入演算法23 3.3萃取演算法26 第四章實驗結 果與分析31 4.1實驗結果31 4.2與其他方法的比較37 第五章結論與未來研究方向42 5.1 結論42 5.2 未來研究方向43 參考文 獻44
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