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研究生:林啟瑞
研究生(外文):Chi-Ruei Lin
論文名稱:利用多邊形近似技術以建立頂點為基礎之適應性物件外形編碼
論文名稱(外文):Adaptive Vertex-Based Shape Coding By Polygonal Approximation
指導教授:謝朝和謝朝和引用關係郭忠民郭忠民引用關係
指導教授(外文):Chaur-Heh HsiehChung-Ming Kuo
學位類別:碩士
校院名稱:義守大學
系所名稱:資訊工程學系
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:2002
畢業學年度:90
語文別:中文
論文頁數:68
中文關鍵詞:外形編碼適應性物件外形邊碼
外文關鍵詞:Shape CodingAdaptive Shape Coding
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近年由於多媒體應用的日益普及,為了達到更有效率,及更多元的多媒體功能,因此以物件為基礎(Object-Based)的視訊壓縮及處理,就成為實現這樣目的的一個最有效的技術。對於物件為基礎的相關技術而言,不論是那一種應用,其中一個重要的議題,就是如何有效而精確的表示物件。因此發展一有效的物件表示法,及其相關的輪廓編碼技術,是一極為重要的課題。
本論文將提出一新的視訊物件多邊形輪廓表示法及其編碼架構。首先利用平滑完全八-連接性來使原始輪廓平滑化,此特性可以減少頂點的數目而不會損失物件品質。其次,本論文提出由精緻到粗略及由粗略到精緻之物件輪廓頂點選擇法。於精緻到粗略之頂點選擇法中,本論文發展出一利用新的權重技術來測量其失真。此法在計算和實現方面均簡單,且其重建出來的物件輪廓亦有良好的品質。在精緻到粗略之頂點選擇法中,本論文提出一新的以區域面積為基礎之測量方式,其可以很容易加入新的頂點。最後,我們提出利用多重動態範圍的方式去修改適應性外形編碼(OAVE)。此法可有效的改進編碼的效率。此外,本論文亦提出一有條件差異性鏈碼編碼法(CDCC),此法可對未分類機率分佈之輪廓編碼。
Recently, with the rapid advance of internet technique, users demand fast, flexible and interactive access to multimedia content. This results in the development of content-based (or object-based) audio-visual processing. Therefore, the representation and coding of audio or video objects become an important research issue.
This thesis presents a new polygonal shape representation and coding for video objects. We first smooth out the original contour using the property of smooth perfect eight-connectivity, which reduces the number of vertexes without loss of quality. Then we propose fine to coarse and coarse to fine algorithms to select the vertex points. In fine to coarse scheme, we develop a new weighting technique to measure the distortion. It is simple for both calculation and implementation, and the reconstructed shape has better quality. In the coarse to fine scheme, we develop a new area-based measure that makes the insertion of a new vertex easy. Finally, we propose a multiple dynamic range to modify the OAVE. It improves the coding performance significantly. In addition we present a lossless vertex coding scheme, called conditional differential chain code(CDCC). The CDCC can encode the contour without any overhead for the classification and probability distribution.
ACKNOWLEDGEMENTS.............................4
摘 要........................................5
第一章 導論..................................13
1.1研究動機與研究目的........................13
1.2論文方法和貢獻............................15
1.3論文結構..................................16
第二章 物件輪廓編碼簡述......................17
2.1數位直線..................................17
2.2多邊形近似之頂點選取方法..................19
2.2.1重複疊代頂點選擇法......................19
2.2.2權重式重複疊代法........................22
2.3物件輪廓壓縮的方法........................24
2.3.1以鄰域為基礎之算術編碼法................24
2.3.2以頂點為基礎之物件輪廓編碼..............25
第三章 以頂點為基礎之適應性物件輪廓編碼法....27
3.1平滑完全八連通性連接之運作................28
3.2以物件為基礎之頂點選擇法..................32
3.2.1由精緻到粗略之物件輪廓描述法............32
3.2.2由精緻到粗略之物件輪廓局部修正..........35
3.2.3由粗略到精緻物件輪廓描述法..............36
3.2.4由粗略到精緻物件輪廓局部修正............41
第四章 頂點編碼..............................43
4.1 適應性物件輪廓頂點編碼法之改良...........43
4.2 條件式差異鏈碼編碼法.....................47
第五章 實驗結果..............................51
5.1頂點選擇的評估及比較......................51
5.2誤差分析..................................56
5.3 MOAVE編碼效益的評估......................59
5.4 平滑完全八連接在CDCC編碼上的效益.........61
第六章 結論與未來研究方向....................63
參考文獻.....................................64
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