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研究生:林惠娟
研究生(外文):Hui-Chuan Lin
論文名稱:壓縮影像之資訊隱藏與查詢技術
論文名稱(外文):A Study of Information Hiding and Image Retrieval Techniques for compressed Images
指導教授:吳憲珠
指導教授(外文):Hsien-Chu Wu
學位類別:碩士
校院名稱:國立臺中技術學院
系所名稱:資訊科技與應用研究所
學門:電算機學門
學類:電算機應用學類
論文種類:學術論文
論文出版年:2006
畢業學年度:95
語文別:英文
論文頁數:66
中文關鍵詞:向量量化數位浮水印樹狀成長結構調色盤影像反叢聚偽裝術影像查詢邊緣走向直方圖
外文關鍵詞:vector quantizationwatermarkingtree growing structurepalette imagede-clusteringsteganographyimage retrievaledge direction histogram
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本論文主要探討數位影像的隱藏技術與查詢技術,將隱藏技術應用於壓縮影像中以達到版權保護、秘密通訊的目的;另以壓縮影像為影像查詢的對象,以達到精確且快速查詢的目的。
首先,本論文的第一個隱藏技術是針對著作權保護提出一個植基於向量量化壓縮影像之數位浮水印技術,將保護象徵的商標、符號、圖騰等訊息藏於欲保護的影像之中,其做法是利用樹狀成長結構將編碼簿分群,再將浮水印資料藏在編碼過程中。本方法技術上不但簡單,其強韌性可安全維護影像的擁有者之所有權,在數位化的世界中,對克服數位媒體所有權之爭議,提供客觀及有效的判定依據。
本論文的第二個技術是利用數位影像來隱藏機密訊息,以達到秘密通訊的目的。本方法是建構於調色盤影像之壓縮技術,運用反叢聚分群技術將調色盤分群,並透過預測方法及旗標值的應用以協助機密隱藏,不僅可提高資訊隱藏的容量,在取出機密的同時並可還原原始影像,改善往常調色盤影像隱藏後失真太多的缺點。為保障資訊在網路上的安全,資料隱藏技術扮演了捍衛資訊的重要角色,即使在不安全的管道傳輸,透過偽裝不易被察覺的特色讓重要資訊多了一層保護,而建構於壓縮影像的隱藏技術不但可節省儲存空間及減少傳輸時間,更可防止偽裝影像因被壓縮而導致資訊的破壞,達到雙重效果。
另一方面,由於網路技術的提昇與生活數位化的需求,多媒體的流通數量幾乎以等比的方式逐漸成長,在多媒體資料庫中,如何快速地找到所需的物件成了衍生的問題。鑒於調色盤影像在網路上已佔有舉足輕重的流通量,在本論文所提的第三個技術是以調色盤影像為對象的查詢技術,透過以調色盤顏色分佈狀況,以及影像邊緣走向之直方圖為特徵的查詢方法,為調色盤影像開創一個查詢技術的開端。其最大的貢獻是直接以調色盤為特徵之一,節省特徵值萃取步驟之繁雜運算,同時此種壓縮格式可為資料庫節省約三分之二的儲存空間,並可為影像查詢技術開啟另一個新領域。
This thesis focuses on information steganography, copyright protection and image retrieval for compressed images. There are three schemes proposed in this thesis. The first proposed scheme is tree growth based watermarking technique. To safeguard the image rightful ownerships, a representative logo or owner information could be hidden in the host image. The operation of this scheme is to divide the codewords into two groups by tree growing structure. The copyright information is embedded during the vector quantization compression. This scheme is simple and robust to protect the copyright efficiently.
The second proposed scheme in the thesis is a steganography technique based on the palette method. The secret information is hidden in a cover image to ensure the transmission security. In this method, the palette colors are divided into two groups using the de-clustering scheme. The median edge detector (MED) predictor and flags are applied in information hiding. It not only increases the hidden capacity, when retrieving the message, but the original image is also recovered at the same time. This method can solve the problem that the palette image has distortion after data hiding.
On the other hand, as a result of rapid Internet growth, the amount of multimedia circulation almost increases by geometric series acceleration. Many content based image retrieval (CBIR) technologies have been proposed in literatures. But few for palette-based image were proposed. However, the palette-based images have been widely used on Internet. In this thesis, a new image retrieval scheme based on palette images is proposed. The palette color (PC) is used as the first index directly and the edge-direction histogram (EDH) as the second index. The best advantage is to leave out large computation. Furthermore, this kind of compression format may save two-thirds of the image space in the database.
Contents

摘 要 I
Abstract III
致 謝 V
Contents VI
List of Tables VIII
List of Figures IX
Chapter 1 Introduction
1.1 Background and Motivation 1
1.2 Cryptography and Information Hiding 2
1.3 Steganographic Technology 6
1.4 Watermarking Technology 7
1.5 Information Hiding Techniques for Compressed Images 8
1.6 Image Retrieval Technology 9
1.7 Thesis Organization 10
Chapter 2 Preliminaries
2.1 Vector Quantization Compression 11
2.2 Lu’s Watermarking Algorithm 12
2.3 Index Color Compression Scheme 14
2.4 Palette-Based Steganographies 15
2.5 Median Edge Detector (MED) Predictor 17
2.6 Content-Based Image Retrieval 18
2.7 Sobel Edge Detective Operator 20
Chapter 3 Tree Growth Based Watermarking Technique
3.1 The Proposed Scheme 22
3.2 Grouping Codewords by the Proposed Tree Growing Structure 23
3.3 Embedding Watermark 25
3.4 Retrieving Watermark 26
3.5 Experimental Results 27
Chapter 4 Reversible Palette Image Steganography Based on De-Clustering and Predictive Coding
4.1 The Process of the Proposed Scheme 33
4.2 Grouping Palette Colors 35
4.3 Embedding Secret Message 37
4.4 Extracting the Message and Recovering the Original Image 39
4.5 Experiment Results 41
Chapter 5 A New Image Retrieval Scheme Based on Palette Image
5.1 The Proposed Scheme 45
5.2 The Palette Color (PC) Feature 46
5.3 The Edge Direction Histogram (EDH) Feature 48
5.4 The Image Retrieval Process 50
5.5 Experimental Results 52
Chapter 6 Conclusions 59
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