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研究生:馬啟強
研究生(外文):Chil-Chenag Ma
論文名稱:應用於視訊壓縮之形狀編碼演算法
論文名稱(外文):The Research of Shape Coding Algorithms for Video Coding
指導教授:陳美娟陳美娟引用關係
指導教授(外文):Mei-Juan Chen
學位類別:碩士
校院名稱:國立東華大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:中文
論文頁數:83
中文關鍵詞:形狀編碼MPEG-4物件導向影像壓縮頂點選取邊緣輪廓j二位元圖
外文關鍵詞:Shape CodingMPEG-4Objetc-basedVideo CompreessionVertex SelectionCoutour-basedBitmap-based
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數位視訊產品及服務目前正以驚人的速度在民生電子、電腦以及通訊工業上積極發展中。在影像視訊儲存與傳輸上,工業界訂定的標準如早期的JPEG,JBIG,MPEG-1,H.261到MPEG-2,H.263等,開啟了多媒體與數位電視的時代。上述這些視訊壓縮標準是以傳統的波形編碼與以區塊為基礎的方式處理影像視訊。MPEG-4是ISO MPEG組織建立的一個通用且有效率的編碼標準,並於1998年 11 月已制訂了解碼端的國際標準規格。MPEG-4除了融合了MPEG-1 和 MPEG-2 影像編碼功能以外,並首先加入以內容為基礎的視訊編碼方式,其採用以物件為基礎的壓縮方法。影像畫面被分割成一些任意形狀的物件平面,並針對物件進行形狀編碼、移動估計與紋理編碼。使用形狀編碼的目的是為了得到較好的影像品質與增加編碼效益。其中,形狀編碼主要分為位元圖形狀編碼與輪廓形狀編碼兩個大方向。
這篇論文中,在輪廓形狀編碼方面,我們也提出了利用極座標方式來減少形狀點數的新技術。我們利用半徑與角度的關係,提出一種新的取點技巧,在失真性之編碼法方面,我們採取面積失真準則技術以達到重建影像品質與位元率之間的良好權衡。而另一方面方面,我們提出以四分樹為基礎的分割與編碼方式應用於位元圖形狀編碼上。位元圖影像本身具有許多相同圖素區域的關連性,所以,以大區塊為單位,再加上四分樹系統特性,可以簡單地將二位元形狀加以編碼。我們為了再進一步地降低編碼位元率,也提出改良式的四分樹形狀編碼的方法,此技巧不僅保留了四分樹系統的簡單性,在編碼效益上也大大的提升。並且我們在各項技術中做了電腦模擬實驗。從實驗結果驗證了我們提出的形狀編碼技術在失真,無失真,低位元率編碼效益與影像品質都比其他的形狀編碼方法好

Anticipating the rapid convergence of telecommunication, computer, and TV/film industries, the just finalized MPEG-4 standard from ISO standardizes algorithms and tools for coding and flexible representation of audio-visual data to meet the challenges of future multimedia applications. In addition to standard MPEG-1 or MPEG-2 like provisions for efficient coding of conventional image sequence, MPEG-4 enables an efficient coded representation of the video data that can be content based, with the aim to use and present the data in a highly flexible way. To enable the envisioned content-based interactive functionalities, the MPEG-4 video standard introduces the concept of video object planes (VOPs). It is assumed that each frame of an input video sequence is segmented into a number of arbitrarily shaped image regions- each of the regions may possibly cover particular image or video content of interest, i.e., describing physical objects or content within scenes. In contrast to the video source format used for the MPEG-1 and MPEG-2 standards, the video input to be coded by the MPEG-4 is thus no longer considered a rectangular region. The input to be coded can be a VOP image region of arbitrary shape and the shape and location of the region can vary from frame to frame. The concept of VOPs involves the problem of shape representation, motion estimation and texture coding. The purpose of using shape is to achieve better subjective picture quality, increased coding efficiency as well as an object-based video representation. There are mainly two categories for binary shape coding: bitmap-based and contour-based.
In this thesis, we propose the quadtree-based shape coding, which exploits the spatial redundancy for bitmap-based shape coding category. The hierarchical structure provides good performance. For contour-based category, the new scheme with polygon approximation in polar coordinates is conducted. The vertex selection and area distortion criterion techniques are included. Computer simulation is conducted. The proposed shape coding algorithms are expected to make compression more efficient and degradation due to errors more graceful. The proposed schemes provide good rate-distortion trading-off. The results indicate that the subjective quality is improved compared to other shape coding methods

中文目錄
摘要…………….……………………………………………….I
誌謝…………………………………………………………...III
目錄…………………………………………………………...IV
第一章 簡介…………………………………………………..V
第二章 形狀編碼技術………………………………………VII
第三章 極座標形狀編碼與實驗結果……………………….XI
第四章 四分樹形狀編碼與實驗結果………………….….XIII
第五章 結論………………………………………………...XV
英文附錄……………………………………………………...XV
英文目錄
Contents
English Abstract………………………………………………………………………..i
Table of Contents……………………………………………………………………..iii
List of Figures…………………………………………………………………………v
List of Tables………………………………………………………………………...viii
1. Introduction….…………………………………………………………………….1
2. Review of Arbitrary Shape Coding Techniques…..………….……………………5
2.1 Basic Theory…………………………………………………………...…5
2.2 Bitmap-based Shape Coding……………………………………………...6
2.2.1 Context-Based Arithmetic Encoding (CAE) …………………….7
2.2.2 Modified MMR (Modified Modified Read) Coding ………….....9
2.3 Contour-based Shape Coding……………………………………………16
2.3.1 Vertex-based Shape Coding…………………………………….16
2.3.2 Object-Level Relative-Location Dynamic Range Adaptation….20
2.3.3 Vertex-Level Octant-Based Dynamic Range Adaptation……….22
2.2.4 Chain Coding …………………………………………………...24
3. Vertex-Based Shape Coding in Polar Coordinates (VBPC)………….…………..25
3.1 Transformation to Polar Coordinates……………………………………26
3.2 A New Vertex Selection Scheme for Polygon Approximation………….27
3.3 Vertex Subsampling with Area Distortion Criterion…………………….30
3.4 Entropy Coding………………………………………………………….32
3.5 Simulation……………………………………………………………….33
4. Quadtree-based Shape Coding Algorithm……………………………………...54
4.1 Quadtree-based Shape Coding Algorithm (QTSC)……………………...55
4.2 Improvement of the QT-based Shape Coding (IQTSC)…………………58
4.3 Simulation……………………………………………………………..…67
5. Conclusions……………………………………………………………………..77
Bibliography……………………………………………………………………….…79

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