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研究生:楊朝程
研究生(外文):Chao-Cheng Yang
論文名稱:以調適性自我組織樹為基礎之影像壓縮技術
論文名稱(外文):ST-ACO:Image Compression Using An Adaptive Self-Organizing Tree Approach
指導教授:蔡正發蔡正發引用關係
指導教授(外文):Cheng-Fa Tsai
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:129
中文關鍵詞:有損式影像壓縮螞蟻演算法有效性演算法多媒體子節點二元樹資料分群
外文關鍵詞:lossy image compressionACO algorithmdynamicdatadata clustering
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多媒體資料的傳輸與保存是現今資訊科技應用上的重要課題,資料壓縮是完成這些目標的一個主要部份。本論文提出一個改良自我組織樹的階層式二元樹搜尋架構演算法;除了運用動態路徑選擇方式(DPTSVQ)以減低樹狀結構的尋搜偏差外,同時也加上螞蟻演算法(ACO)的概念,利用每個訓練資料點在搜尋最適分群中心點的過程,動態改變節點上的路徑選擇門檻值,逐步將樹狀結構中,各中介節點與其子節點間的相似程度描繪出來,使其建構成具有階層式的資料分群,並應用此建構出的分群原則於影像壓縮的量化編碼。
本論文與著名的TSVQ演算法、GLA/LBG演算法、S-TREE演算法、GATSM演算法比較“有損失式影像壓縮(Lossy Image Compression)”的壓縮品質(PSNR)及編碼訓練時間(Codebook Time)有效性,實驗結果顯示,本論文提出的方法在壓縮品質上比樹狀結構的TSVQ演算法、S-TREE演算法有較佳的PSNR值,訓練時間上則比LBG、GATSM所需的時間要短。
Multimedia data transmission and storage are very important topics in information technology applications nowadays. Data compression is the key element to achieve these goals. In this thesis, a modified self-organizing tree algorithm is proposed, which is a binary tree searching method. We embed not only the dynamic path selection method to reduce the tree searching bias, but also the concept of ACO algorithm to dynamically change the threshold value in the traversed nodes listed in the searching path, which is utilizing the searching appropriate centroid process performed via each training vector. Depicting the similarity between each inner node and its child nodes of the tree structure progressively. As a result, hierarchical clusters will be constructed and this clustering rule can be used in encoding step of vector quantization to achieve the image compression.
In addition, we compare the proposed method with TSVQ, GLA/LBG, S-TREE and GATSM algorithm in image quality and codebook training time. The simulation results show that the proposed method outperforms those tree-structed algorithms, such as TSVQ, S-TREE in reconstruction image quality and takes less training time than those in LBG algorithm and GATSM algorithm.
摘 要 I
Abstract II
誌 謝 IV
目 錄 V
圖索引 VII
表索引 X
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究範圍與限制 2
1.4 論文架構 3
第2章 文獻回顧 4
2.1 S-TREE演算法 7
2.2 螞蟻演算法 14
2.3 GLA與LBG演算法 17
2.4 GATSM演算法 20
2.5 樹狀結構向量量化演算法(TSVQ) 22
2.6 多路徑搜尋演算法(Multipath TSVQ) 25
2.7 快速搜尋及編碼演算法(EAWFC) 27
2.8 動態路徑選擇的演算法 29
2.9 資料量化壓縮的發展背景 31
2.9.1 向量量化Vector Quantization的介紹 31
2.9.2 影像壓縮品質衡量的常用方法 35
2.9.3 影像壓縮的常用方法 37
2.9.4 向量量化(Vector Quantization)的分類 38
第3章 研究方法 46
3.1 研究模式推導 46
3.2 本論文提出的ST-ACO演算法 48
第4章 實驗結果與討論 64
4.1 實驗環境 64
4.2 資料取得 65
4.3 實驗流程圖說明 66
4.4 實驗一 70
4.5 實驗二 96
4.6 實驗三 106
4.7 實驗四 110
第5章 結論與未來研究方向 122
5.1 結論 122
5.2 未來研究方向 127
參考文獻 130
附 錄 134
作者簡介 140
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