臺灣博碩士論文加值系統

向量量化(VQ)在低損資料壓縮應用上有著極高的壓縮率，所以近來以向量量化為基礎的浮水印研究也漸漸的成為數位浮水印相關研究領域中的一個分支。在本篇論文中，我們提出了一種植基於向量量化的浮水印最佳化技術，利用遺傳演算法(GA)來克服編碼簿中每個編碼字的索引值分配問題，使得藏入浮水印後的影像，能適合在具有通道雜訊的通道上傳輸。經由測試數個不同的影像後，實驗結果顯示出我們提的方法對於通道雜訊所產生的影響，能有較佳的強健性。而且我們比較現存文獻中，其他方法所藏入浮水印的影像品質後發現，我們提出的方法幾乎有著相同的影像品質，同時並能保有較佳的強健性，這也證明了我們所提的植基於向量量化之浮水印技術對於保護版權有著良好的效果。

Vector quantization (VQ) has been distinguished for its high compression rate in lossy data compression applications. And VQ-based watermarking plays a newly developed branch in digital watermarking research fields. In this paper, we propose optimized schemes for VQ-based image watermarking. We overcome the VQ index assignment problem with genetic algorithm (GA), which is suitable for transmitting the watermarked image over noisy channels. We obtain better robustness of the watermarking algorithm against the effects caused by channel noise in our simulations after inspecting the results from several test images. In addition, to compare with existing schemes in literature, the watermarked image quality in our scheme has approximately the same quality, with better performance in robustness, to the schemes proposed by other researchers. This also proves the effectiveness of our proposed schemes in VQ-based image watermarking for copyright protection.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章 緒論
1.1 前言 1
1.2 研究動機 1
1.3 論文架構 2
第二章 數位浮水印簡介
2.1 浮水印的分類 4
2.2 基本浮水印的特性 8
2.3 基本浮水印的架構 10
2.4 數位浮水印演算法的效能評估 11
2.5 常見的浮水印攻擊簡介 15
第三章 向量量化
3.1 原理簡介 18
3.2 向量量化編碼與解碼 20
3.3 編碼簿的產生方式 21
3.4 快速計算距離的方式 24
3.5 其它向量量化演算法 26
第四章 遺傳演算法
4.1 簡介 30
4.2 遺傳演算法的架構 31
4.3 選擇的方式 36
4.4 交配的方式 37
4.5 突變的方式 41
4.6 遺傳演算法的優缺點 43
第五章 針對非記憶性二元對稱通道所提之浮水印技術
5.1 所提之浮水印最佳化技術簡介 45
5.2 產生適合藏匿浮水印的編碼簿 46
5.3 浮水印的藏匿步驟 50
5.4 傳輸通道簡介 52
5.5 產生具抵抗通道浮水印失真之編碼簿 56
5.6 浮水印的萃取步驟 60
5.7 改良式的浮水印最佳化技術 61
第六章 實驗結果
6.1 實驗說明 63
6.2 實驗結果(一) 65
6.3 實驗結果(二) 67
6.4 實驗結果(三) 69
第七章 結論
7.1 總結 73
7.1 未來展望 74
參考文獻 75
自傳 82

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