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研究生: 洪櫻慈
研究生(外文): Ying-Tzu Hung
論文名稱: 基因演算法在向量量化之應用
論文名稱(外文): Application of Genetic Algorithm to Vector Quantization
指導教授: 潘欣泰鄭志宏
指導教授(外文): Shing-Tai PanJ. H. Jeng
學位類別: 碩士
校院名稱: 樹德科技大學
系所名稱: 資訊工程學系
論文出版年: 2005
畢業學年度: 93
語文別: 中文
論文頁數: 62
中文關鍵詞: 影像壓縮向量量化遺傳演算法分類向量量化
外文關鍵詞: Image compressionVQGACVQ
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  • 被引用:0
  • 點閱:5
  • 評分:*****
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在失真壓縮技術中向量量化(VQ)是一種高壓縮率的編碼技術,但傳統向量量化的方法必須花費大量的編碼時間與運算量,因此許多學者紛紛提出改善方法,而在搜尋全域最佳解的技術裡基因演算法(GA)是人們相當認同的演算法之一,因此本論文將改善GVQ演算法與提出一個新的基因分類向量量化(GCVQ)演算法,由於傳統的編碼簿產生,大多局限於從訓練集中產生,而這些碼向量只具有訓練集的影像特徵,未能適用所有的影像,所以GCVQ演算法以產生全域編碼簿為主旨,結合CVQ與MRVQ的技術。
Vector quantization (VQ) is an efficient image-coding technique because of its high compression ratio for distorted compression technique.  However, the method of VQ always costs much encoding time and the large amount of MSE computations.  Consequently, some optimization methods are used to speedup the training procedure.  It is well known that Genetic Algorithm (GA) is an efficient global search method.  Hence, this thesis will improve GVQ algorithm and propose a new gene classification vector quantification (GCVQ) algorithm.  In the past the codebook in VQ is mostly produced from training set.  Consequently, the codevectors of codebook posess only the characteristic of the image which the training set is generated.  The codebook could not be suitable for all images.  So in this thesis, CVQ and MRVQ are used to implement the GCVQ algorithm, and it will produces the global codebook.
中文摘要  ....................................  i
英文摘要  ....................................  ii
誌謝  ....................................  iii
目錄  ....................................  iv
表目錄  ....................................  v
圖目錄  ....................................  vi
符號說明  ....................................  vii
一、  緒論  1
二、  向量量化  4
2.1  向量量化編碼流程  4
2.2  向量量化解碼流程  5
2.3  影像品質評估之方法  6
2.4  產生編碼簿-LBG演算法  7
2.5  LBG演算法實驗結果  9
三、  分類向量量化  15
3.1  分類向量量化背景  15
3.2  分類向量量化分類類型  16
3.3  設計分類向量量化編碼簿  17
3.4  分類向量量化編解碼流程  18
3.5  四類的分類向量量化  19
3.6  十六類的分類向量量化  23
四、  基因演算法  27
4.1  基因演算法參數介紹  27
4.2  染色體編碼  28
4.3  基因演算法基本運算子  29
4.3.1  選擇  29
4.3.2  交配  31
4.3.3  突變  33
4.4  基因演算法終止條件  34
4.5  基因演算法與其它演算法之差異性  34
五、  基因演算法與向量量化之應用  36
5.1  基因向量量化(GVQ)演算法  36
5.2  改善GVQ演算法之方法  40
5.3  GVQ演算法與改善之方法實驗結果  41
5.4  基因分類向量量化(GCVQ)演算法  45
5.4.1  GCVQ演算法編碼流程  47
5.4.2  GCVQ演算法解碼流程  49
5.4.3  產生GCVQ演算法編碼簿  50
5.5  GCVQ演算法實驗結果  53
六、  結論與未來研究方向  59
參考文獻  ....................................  60
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