植基於學習與紋理的演算法及其在影像壓縮和半色調影像回復的應用

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研究生：	黃詠淮 Yong-Huai Huang
論文名稱：	植基於學習與紋理的演算法及其在影像壓縮和半色調影像回復的應用 Novel Learning- and Texture-Based Approach with Applications to Image Coding and Inverse Halftoning
指導教授：	鍾國亮 Kuo-Liang Chung
口試委員:	貝蘇章 Soo-Chang Pei 陳宏銘 Homer H. Chen 范國清 Kuo-Chin Fan 張寶基 Pao-Chi Chang 廖弘源 Mark Liao 陳世旺 Sei-Wang Chen 廖慶榮 Ching-Jong Liao 洪西進 Shi-Jinn Horng
學位類別：	博士 Doctor
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	115
中文關鍵詞：	算術編碼、文件鄰屬、決策樹、離散餘弦轉換、誤差擴散影像、半色調影像回復、最小平方法預測法、lookup tree table 、無失真壓縮、多重模版、多重學習視窗、預測編碼、紋理、向量量化、小波轉換
外文關鍵詞：	Arithmetic coding, context, decision tree, DCT, error–diffused images, inverse halftoning, least-square–based prediction scheme, lookup tree–table, lossless compression, multiple–template, multiple–window, predictive coding, texture, vector quantization, wavelet transform.
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上一筆

在影像處理中，紋理是一個相當重要的特徵且已經被廣泛的使用在多種應用上。以紋理分類為基礎，本篇論文提出了嶄新且有效的影像/視訊像處理演算法。這些演算法包括：針對誤差擴散影像(error diffused images)所設計的適應性算數編碼演算法(adaptive arithmetic coding)，針對灰階影像所設計的預測編碼演算法(predictive coding)，以及將半色調影像建為灰階影像的半色調影像回復演算法(inverse halftoning algorithm)。

針對文件鄰屬式(context)算數編碼技術，我們首先提出一個區塊式紋理學習機制來擷取影像中最具代表性的紋理特徵，並根據這些紋理特徵建立多重模版(multiple-template)。接著，利用已經建立的多重模版，本篇論文提出一個植基於紋理與多重模版的算數編碼技術來對誤差擴散影像進行無失真壓縮。在我們所提出的方法中，輸入的誤差擴散影像會先被分割成多個不重疊的區塊，依照各個區塊的紋理特徵，我們從多重模版中選擇一個最佳模版以便執行文件鄰屬式算數編碼。實驗結果顯示，與JBIG壓縮標準、Reavy和Boncelet以及Lee和Par所提出的演算法相較之下，我們所提出的演算法能夠達到更高的壓縮比。

針對預測編碼，為了提升最小平方法(least square)預測的準確性，我們提出一個植基於紋理的學習機制以便依據不同的影像內容建立多重學習視窗(multiple-window)。接著，利用已經建立的多重學習視窗，本篇論文提出一個植基於紋理與多重學習視窗的最小平方預測法來對灰階影像進行無失真壓縮。在我們所提出的方法中，針對輸入的灰階影像每個像素的紋理特徵，我們從多重學習視窗中選擇一個最佳學習視窗以便與用最小平方法來預測該像素的灰階值。實驗結果顯示，與先前所提出的預測方法相較之下，我們所提出的演算法能夠達到更準確的預測。

針對半色調影像回復，我們根據變異數增益決策樹(variance gain-based decision tree)，提出了植基於紋理以及變異數增益決策樹的學習機制來建立一個lookup tree table以便進行半色調影像的回復。在重建灰階影像的過程中，對於影像的不平滑區域，我們使用了一個植基於邊的精煉過程(edge-based refinement)來的提昇重建灰階影像的品質。實驗結果顯示，與先前所提出的三種半色調影像回復演算相比之下，我們所提出的演算法能夠達到更高的影像品質。

Texture is an important feature in images and has been widely used in many applications. Based on the classified textures, this thesis presents a novel learning–and texture–based approach to design more efficient image and video processing algorithms such as the adaptive arithmetic coding for error–diffused images, the predictive coding for gray images, and the inverse halftoning algorithm to reconstruct gray images from halftones.

For context–based arithmetic coding, the block– and texture–based training process is first applied to train the multiple–template according to the most representative texture features. Based on the trained multiple–template, we next present a texture– and multiple–template–based (TM–based) arithmetic coding algorithm for lossless compression of error–diffused images. In our proposed TM–based algorithm, the input image is divided into many blocks and for each block, the best template is adaptively selected from the multiple–template based on the texture feature of that block. Experimental results demonstrate that the compression ratio of our proposed TM–based algorithm is
superior to that of joint bilevel image group (JBIG) standard and the previous algorithms proposed by Reavy and Boncelet and by Lee and Park.

For predictive coding, to improve the accuracy of the least square–based prediction scheme, we present a new texture–based training process to construct the multiple–window for various image contents. Based on the trained multiple–window, the texture–and multiple–window–based (TMW–based) prediction scheme is presented for lossless compression of images. In our proposed TMW–based scheme, for each pixel of the input image, the best training window is adaptively selected from the multiple–window according
to the texture feature. Experimental results demonstrate that the accuracy of our proposed TMW–based prediction scheme is better than that of the previous LS–based prediction scheme.

For inverse halftoning, based on our proposed variance gain–based DT (VDT), a texture and VDT (TVDT)–based training process is presented to construct a lookup tree–table which will be used in the reconstructing process. In the reconstructing process, to enhance the quality of the non–smooth regions in the reconstructed image, we propose
an edge–based refinement scheme to enhance the quality of the reconstructed gray image. Experimental results demonstrate that our proposed TVDT–based inverse halftoning algorithm has the highest image quality when compared to the currently published three inverse halftoning algorithms, such as the DT–based algorithm, the lookup table–based algorithm, and the edge–and lookup table–based algorithm.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1
1 Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Motivations and purposes. . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Organization of the thesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Problem Definitions and the Texture Considerations. . . . . . . . . . . . . 6
1 Context–based arithmetic coding. . . . . . . . . . . . . . . . . . . . . 6
1.1 Basic definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Texture consideration. . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Predictive coding. . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Basic definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Texture consideration. . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Inverse halftoning . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1 Basic definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Texture consideration. . . . . . . . . . . . . . . . . . . . . . . . . 22
Texture– and Multiple–Template–Based Algorithm for Lossless Compression
of Error–Diffused Images. . . . . . . . . . . . . . . . . . . . . . . . . . 27
1 Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 The past four works: JBIG, BACIC, PACIC, and FACIC algorithms. . . . . . 30
2.1 The JBIG and the BACIC algorithm . . . . . . . . . . . . . . . . . . . 30
2.2 The PACIC algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.3 The FACIC algorithm  . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 Our proposed TMCIC algorithm . . . . . . . . . . . . . . . . . . . . . . 36
3.1 Training stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Encoding and decoding stages . . . . . . . . . . . . . . . . . . . . . 42
4 Experimental result. . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1 Results of our proposed training stage . . . . . . . . . . . . . . . . 45
4.2 Compression performance comparison . . . . . . . . . . . . . . . . . . 47
Texture– and Multiple–Window–Based Prediction Scheme for Predictive
Coding . . . . . . . . . . . . . . . .  . . . . . . . . . . . . . . . . . .  52
1 Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2 The Prediction Schemes for Lossless Image Compression. . . . . . . . . . 54
2.1 Context–based prediction scheme . . . . . . . . . . . . . . . . . . . 54
2.2 LS–based prediction scheme  . . . . . . . . . . . . . . . . . . . . . 56
3 Our Proposed TMW–Based Prediction Scheme. . . . . . . . . . . . . . . . 59
3.1 Training stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2 Prediction stage . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Efficient Inverse Halftoning Using Variance Gain–, Texture– and Decision
Tree–Based Learning Approach . . . . . . . . . . . . . . . . . . . . . . .  70
1 Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2 Relevant PastWorks . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
2.1 The LIH algorithm  . . . . . . . . . . . . . . . . . . . . . . . . . . 73
2.2 The ELIH algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 75
2.3 The DTIH algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3 The Proposed TVDTIH Algorithm  . . . . . . . . . . . . . . . . . . . . . 79
3.1 The proposed variance gain–based DT (VDT) . . .   . . . . . . . . . . 81
3.2 The construction of approximate VDT. . . . . . . . . . . . . . . . . . 84
3.3 The classification and codebook generation for VDT–leaf . . . . . . . 86
3.4 The reconstructing process . . . . . . . . . . . . . . . . . . . . . . 89
3.5 Edge–based refinement scheme. . . . . . . . . . . . . . . . . . . . . 90
4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Conclusions . . . .  . . . . . . . . . . . . . . . . . . . . . . . . . .  106

                                

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