可逆式浮水印技術是能將藏有浮水印的數位影像, 在進行擷取出浮水印的動作之後, 仍然能無失真地還原成原始影像.它適用於一些對影像品質要求較高的領域，例如醫學與軍事影像等。在本論文裡, 我們提出一個多重預估值的像素預估法. 它利用相鄰的像素, 求得12個候選預估值, 再根據一個選擇演算法, 求得適當的預估值. 在這個選擇演算法裡, 我們除了使用原始像素與相鄰像素之間的相關性外, 另外, 也使用了原始像素作為預估時的參數之一, 以增加預估的正確性.
實驗結果顯示, 我們提出的方法比Sachnev的方法所得的預估誤差直方圖的變異數減小44.2%, 而且, 以Lena影像為例, 浮水印在0~0.04bpp與0.04~0.5bpp的嵌入量下, 我們的方法也較Sachnev et al. 的方法所得的PSNR分別平均高約1.47 dB, 與1.1 dB. 因此, 這個方法是一個適用於中、低浮水印嵌入量, 並能得到極佳嵌入浮水印影像品質的方法。
關鍵字：可逆式浮水印, 浮水印, 預估值, 直方圖平移法。

Reversible watermarking techniques extract the watermark and recover the original image from the watermarked image without any distortion. They have been applied for those sensitive fields, such as the medicine and the military. In this thesis, a novel watermarking algorithm using multi-prediction values has been proposed. It exploits the correlation between the original pixel and the neighboring pixels to obtain twelve prediction candidates, and then selects a candidate as the prediction value according to the original pixel and the temporary prediction value. Due to the algorithm use the original pixel as one of the parameters to decide the prediction value, our prediction values are obtained with great precision.
The experimental results reveal that the performance of our proposed method outperforms that proposed by Sachnev. For example the variance of the prediction errors histogram obtained by the proposed method is less than that obtained by the algorithm proposed by Sachnev et al. about 44.2%; the mean PSNR greater than about 1.47 dB and 1.1 dB under the watermark capacity 0~0.04 bpp and 0.04~0.5 bpp, respectively. Therefore, the proposed method is especially appropriate for embedding watermark in low or medium capacity.
Keyword： reversible watermarking, watermarking, prediction, histogram shifting.

Abstraction...................................................................ii
Table of contents…………………………............…iii
List of figures…………………….......................……v
Chapter 1 Introduction...............................................1
1.1 Watermarking introduction.................................1
1.2 Reversible watermarking schemes.................2
1.2.1 Reversible watermarking schemes by using difference expansion..................................................2
1.2.2 Reversible watermarking schemes by using histogram bin shifting.................................................3
1.2.3 Reversible watermarking schemes by applying data compression........................................................4
1.2.4 Reversible watermarking schemes by using prediction error and histogram bin shifting.............5
1.3 Motivation and objectives.....................................6
1.4 Organization of the Thesis...................................7
Chapter 2 Related Works............................................8
Chapter 3 Proposed Method......................................12
3.1 Prediction using sub-sample image pattern....12
3.1.1 Sub-sample image and sorting.......................12
3.1.2 Prediction using multi-prediction values........14
3.2 Watermark embedding and extraction...............19
3.2.1 Histogram shift using prediction error............19
3.2.2 Threshold values.................................................22
3.3 Solve the problem of overflow and underflow....24
3.4 Encoding and Decoding........................................27
3.4.1 Encoding process................................................28
3.4.2 Decoding process................................................29
Chapter 4 Experimental results...................................31
Chapter 5 Conclusions and future works..................41
5.1 Conclusions..............................................................41
5.2 Future works..............................................................41

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