視覺之谷：影像處理相關研究改良之建模、量化、補償觀點與思維

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研究生：	劉雲夫 Yun-Fu Liu
論文名稱：	視覺之谷：影像處理相關研究改良之建模、量化、補償觀點與思維 The Valley of Vision: Perspectives and Thoughts of Modeling, Quantizing, and Compensating for Image Processing Improvement Related Studies
指導教授：	郭景明 Jing-ming Guo
口試委員:	李建德 Jiann-Der Lee 楊家輝 Jia-hui Yang 林維暘 Wei-yang Lin 陳俊宏 Chun-Hung Chen 李佩君 Pei-jun Li
學位類別：	博士 Doctor
系所名稱：	電資學院 - 電機工程系 Department of Electrical Engineering
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	295
中文關鍵詞：	視覺、半色調、浮水印、資訊隱藏、影像強化
外文關鍵詞：	Vision, halftoning, watermarking, data hiding, image enhancement
相關次數：	點閱：376 下載：6
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透過人眼所接收到之資訊並非完美反映出真實情況，取而代之的，是為根據人眼之「喜好」所「修正」之接收結果。高度相關於人眼特性的數位影像處理領域，如果根據其特性取得受到人眼偏愛的、而非僅忠實呈現原始資訊的成果，將亦同等效於取得更佳的使用者感受。在本論文中共涉及到三項主題，包含半色調技術(halftoning)、浮水印技術(watermarking)、以及影像對比強化技術(image contrast enhancement)之共六項子獨立研究課題。此些課題之改良多少涉及到利用前述之視覺非完美特性，並配合諸如建模、量化、補償等方式進行效能上之強化。對於其中兩項半色調技術方面之研究，皆主要於半色調技術中之dot diffusion (DD)技術進行針對性改良，分別稱為co-optimized DD以及near-aperiodic DD (NADD)，且其皆巧妙利用到人眼之低通特性作為視覺還原原始資訊的主要中心思維。另外，所屬於浮水印技術之三項研究項目，分別以半色調或block truncation coding (BTC)壓縮後之影像作為表現型。此對應之成果分別稱為overall minimal-error searching (OMES)、complementary hiding error-diffused BTC (CHEDBTC)、以及majority-parity-guidance EDBTC (MPG-EDBTC))等。本三項技術分別在維持視覺上對於原始訊號之相似感受的同時，進行對於隱藏資料的容納量與強健性等等訴求之考量。對於影像對比強化方面，傳統技術所附帶之過度強化所造成之視覺感受劣勢，受到所提出技術parametric-oriented histogram equalization (POHE)以及corrected POHE (CPOHE; 此另特別強化POHE之對比特性)之近似以抑制，同時大幅降低所需求之計算複雜度。本論文提供之成果，預計可應用至印刷產業、隱藏資料傳遞及版權保護等相關訴求、以及近年熱門的圖像識別與電腦視覺等類似之應用科學領域。

The imperfect information received from human eyes reflects the practical situation as known; instead, the perception is modified by personal “tendency.” Digital image processing highly relies on human eyes’ property, if a resultant involves this consideration or thought, even though it is dissimilar to its source from a signal similarity point of view, a better perception quality is still available by the prospective viewers. This dissertation includes three subscopes under this image processing discipline: halftoning, watermarking, and image contrast enhancement, and moreover, totally six individual research works are related. In fact, these works more or less utilize the aforementioned imperfect feature of human eyes for reaching the purposes of giving some further improvements by thoughts of modeling, quantizing, and compensating. Among these, two of the six works, namely co-optimized dot diffusion (DD) and near-aperiodic DD (NADD), attempt improving the printed dot distribution of the DD halftoning technique for yielding a higher visual quality by exploiting the low-pass property of human visual system. Furthermore, three of the six works, namely overall minimal-error searching (OMES), complementary hiding error-diffused block truncation coding (CHEDBTC), and majority-parity-guidance EDBTC (MPG-EDBTC)), consider either halftone patterns or the BTC compressed images as the phenotype of the results after their watermarking processes. These methods lay on ground of great visual marked image quality to reach either high data capacity or robustness as their purposes. For the last contrast enhancement subscope the parametric-oriented histogram equalization (POHE), it is proposed to greatly reduce the complexity as well as ease the artifact of over enhancing induced by the former local methods. In addition, a modified form of the POHE named correct POHE (CPOHE) is also presented for yielding a higher contrast effect by sacrificing somewhat processing efficiency. Overall, these six efforts are able to be potentially applied on printing industry, the appeals of secret data transmission and copyright protection, photo editing, or the most attractive subjects of pattern recognition, computer vision in recent years.

CHAPTER 1 INTRODUCTION 1
1. Motivation 1
2. Contributions 4
3. Organization 18

CHAPTER 2 HALFTONING 20
1. Background and Former Researches 21
1.1. Direct binary search 22
1.2. Error diffusion 27
1.3. Dot diffusion 29
1.4. Ordered dithering 34

2. Improved Dot Diffusion by Diffused Matrix and Class Matrix Co-optimization 36
2.1. Performance evaluation 36
2.2. Diffused weighting and class matrix co-optimization 38
2.3. Experimental results 43
2.4. Preliminary summary 57
3. New Class Tiling Design for Dot-Diffused Halftoning 59
3.1. Optimized near-aperiodic dot diffusion 59
3.1.1. Class tiling rearrangement 61
3.1.2. Optimization for the CM and DM 67
3.2. Experimental results 71
3.3. Preliminary summary 81
4. Summary 82

CHAPTER 3 WATERMARKING 84
1. Brief Introduction and Prior Works 85
1.1. Halftoning based techniques 87
1.1.1. Embed without halftoning 88
1.1.2. Embed with halftoning 93
1.2. BTC based techniques 96
1.2.1. Without marked images 99
1.2.2. With marked images and the use of halftoning-based BTC 101
1.2.3. With marked images and uncategorized manners 103
2. Halftone-Image Security Improving using Overall Minimal-Error Searching 106
2.1. Performance evaluations 107
2.2. Data hiding with overall minimal-error searching 110
2.2.1. ED-based encoder 111
2.2.2. LMS-based encoder 117
2.2.3. Substitution table (S-table) optimization procedure 119
2.3. PS effect correction 123
2.4. Experimental results 125
2.5. Preliminary summary 140
3. High Capacity Data Hiding for Error-Diffused Block Truncation Coding 141
3.1. Error-diffused block truncation coding (EDBTC) 141
3.2. Complementary hiding error-diffused block truncation coding 145
3.2.1. Encoder 145
3.2.2. Decoder 149
3.2.3. Multiple watermarks embedding extension 150
3.3. Experimental results 151
3.4. Preliminary summary 173
4. Joint Compression/Watermarking Scheme using Majority-Parity Guidance and Halftone-based Block Truncation Coding 174
4.1. Performance evaluation 174
4.2. Error-diffused block truncation coding 175
4.3. Majority-parity-guided error-diffused block truncation coding 179
4.3.1. MPG-EDBTC encoder 179
4.3.2. MPG-EDBTC decoder 183
4.3.3. Parameters 184
4.3.4. Attacks 192
4.3.4.1. Experimental results 192
4.3.4.2. Theoretical analyses 200
4.4. Multiple watermarks embedding 207
4.5. Preliminary summary 213
5. Summary 214

CHAPTER 4 *IMAGE CONTRAST ENHANCEMENT 216
1. Background and Previous Efforts 216
2. Parametric-oriented histogram equalization (POHE) 227
2.1. Traditional LHE 227
2.2. Concept description 228
2.3. Simplification 232
2.4. Implementation 233
3. Corrected parametric-oriented histogram equalization (CPOHE) 234
3.1. Classification 235
3.2. Contrast compensation 239
3.3. Approximation 240
3.3.1. μ_(i,j)^opt prediction 240
3.3.2. Symmetric correction 246
3.3.3. Integration 247
3.3.4. Implementation 250
4. Experimental results 251
5. Summary 257

CHAPTER 5 CONCLUSIONS 259

REFERENCES 261
BIOGRAPHY 268

                                

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