臺灣博碩士論文加值系統

隨著資訊化社會與網際網路的蓬勃發展，使得現今數位資訊的大量流通，資訊隱藏就成了備受重視的一項議題。而資訊隱藏的優劣主要的關鍵在於偽裝影像的品質與隱藏量、本碩士論文將分別使用參考點位、調整參數值以及針對圖像特性增加其適應性等三種方法，皆可以有效提高圖像預測的準確度，使得直方圖的高點大幅增加，藉此提升資訊隱藏中的資訊隱藏量。根據我們的實驗結果，我們的方法平均比鄭等人的方法增加了約12%的資訊隱藏量。

In this thesis, we use gradient adjacent prediction (GAP) to propose reversible data hiding based on histogram with three different type methods. Our methods can increase the amount of hidden data, and keep higher PSNR of stego-image. Experimental results have shown that the proposed method increases the amount of hidden data by 12% on the average as compared with Jang et al.’s method.

摘要 i
目錄 iii
表目錄 iv
圖目錄 v
一、 導論 1
1.1 前言 1
1.2 資訊隱藏 1
1.3 全文架構 3
二、 相關研究 4
2.1 九宮格式直方圖的可逆式資訊隱藏技術[19, 28] 4
2.2 基於相鄰梯度預測法之直方圖可逆資訊隱藏技術 7
三、 研究方法 12
3.1 增加參考點位 12
3.2 調整參數值 16
3.3 針對圖像特性增加其適應性 17
四、 實驗結果 20
4.1 增加參考點位法 20
4.2 調整參數值法 22
4.3 針對圖像特性增加其適應性法 23
4.4 實驗結果分析 26
五、 結論 27
六、 參考文獻 28

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