在可逆式資訊隱藏中，預先預測是一個很好的方法可以有效的將機密訊息藏入影像中。在本論文中，我們提出一個可逆式的資訊隱藏基於多種預測方法以及使用變異數的方法。我們的方法中，每一層在藏入機密訊息之前我們會有兩種策略以及四種預測方式。首先，如果我們的目標是高藏量便計算各種預測方式的藏量；另外，如果我們的目標是高品質便計算各種預測方式的效率比以選擇該使用那種預測方式。當確定選擇哪種預測方式之後，基於變異數所算出的門檻值將用來確定那些像素應該加入位移和嵌入。如果該區域複雜度小於門檻值，則像素將參加機密藏入或者位移；反之，則像素不會參與嵌入及位移的過程。因此，某些像素將避免執行位移的過程，這會減少影像的失真。經由實驗結果可以發現我們的藏量及品質都高於其他的研究。

Reversible data hiding based on prediction methods is a good technique that can hide secret bits into cover images efficiently. In this study, we propose a reversible data hiding method based on multiple prediction methods and local complexity. In our proposed method, before we embed the secret message in one level, we evaluate four prediction methods by two ways to decide which prediction method will be used. First, if we want to achieve high-capacity, the prediction methods’ capacities are calculated. Second, if we want to achieve high-quality, the prediction methods’ efficiency ratios are calculated. When the selected prediction method is applied, a threshold based on local complexity is used to determine which pixel should join the shifting and embedding process. If the local complexity is smaller than the threshold, the pixel will be taken for message hiding or pixel shifting; otherwise, the pixel will quit joining the process of data concealing and pixel shifting. Therefore, more pixels will avoid executing the process of pixel shifting. It results to the stego-images with lower distortion. The experimental results show that our embedding capacity and quality are superior to other approaches.

Acknowledgements i
中文摘要 ii
Abstract iii
Contents iv
List of Figures v
Table of Contents vi
Chapter 1 Introduction to Reversible Data Hiding Techniques 1
1.1 Background 1
1.2 Organization of Thesis 2
Chapter 2 Literature Review 4
2.1 Yang-Tsai’s interleaving prediction method 4
2.2 Lee et al.’s prediction-error expansion method 6
2.3 Li et al.’s general framework method 7
Chapter 3 Our proposed method 9
3.1 Data Hiding Algorithm 9
3.2 Data Extracting and Recovering Algorithm 15
3.3 Overflow/Underflow 16
Chapter 4 Experimental results 18
Chapter 5 Conclusions 26
References 27

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