臺灣博碩士論文加值系統

碎形影像壓縮於具有高壓縮比、低失真率及解壓縮快速的優點，但是碎形影像壓縮在編碼過程中必須進行窮舉搜尋，使得編碼過程過於冗長。若能夠有效減少搜尋的範圍，便能夠減少編碼所需耗費的時間。藉由將定義域區塊分群的方式，能夠將編碼過程中的搜尋範圍侷限在某一特定的群落之中，有效地降低所需的比對次數，進而達到縮短編碼時間的目的。
在本論文中，採用自組織映射圖網路進行定義域區塊的分群，將定義域區塊轉換為頻率域訊號，並以少量的頻率域係數做為自組織映射圖的訓練依據，建構出一個頻率域訊號的自組織映射圖分群網路，再延伸建立一個空間域訊號之自組織映射圖分群網路。透過頻率域訊號訓練所建立的自組織映射圖網路能夠將區塊依據邊緣方向進行分群，而所延伸建立之自組織映射圖網路亦保留了大部分的此一特性。自組織映射圖網路有效地加快了編碼的速度，並維持了影像品質，而透過空間域訊號的分群方式節省了編碼過程當中必須將影像區塊轉換為頻率域訊號所耗費的時間。

Fractal Image Compression possesses the advantages of high compression ratio, low loss ratio, and fast decompression process. The use of exhaustive search in the encoding process results in a long encoding time. However, the encoding time can be reduced with a limiting searching space. Searching space can be limited to a certain cluster through clustering the domain pool to effectively shorten the comparison time and achieve the goal of reducing encoding time.
In this paper, the clustering of domain pool is implemented by Self-Organizing Map. Each domain block is transformed to frequency domain, and a small amount of AC coefficients is used to train the SOM to construct a frequency-based SOM, and then transforms the frequency-based SOM to construct a new spatial-based one. The frequency-based SOM can cluster domain blocks according to edge direction, and the spatial-based one retains most of the feature. The SOM not only accelerates the encoding speed effectively, but also preserves the image quality. And the spatial-based clustering method saves the time on transforming image blocks to frequency domain during encoding process.

第1章 緒論 1
第2章 碎形影像壓縮 3
2.1 碎形影像壓縮概念 3
2.2 碎形影像壓縮編碼流程 4
2.3 碎形影像壓縮解碼流程 7
第3章 自組織映射圖網路 9
3.1 自組織映射圖網路概念 9
3.2 自組織映射圖網路之結構 9
3.3 自組織映射圖網路之建構流程 11
3.4 自組織映射圖網路分群流程 13
第4章 使用頻率域的分群法則 14
4.1 頻率域區塊的分類 14
4.2 區塊的分群 15
4.3 基於DCT分群結果建立SOM網路 18
第5章 空間域邊緣方向自組織映射圖 21
5.1 演算法概念與流程 21
5.2 自組織映射圖的建立 22
5.2.1 頻率域邊緣方向自組織映射圖的建立 22
5.2.2 空間域邊緣方向自組織映射圖的建立 25
5.3 以自組織映射圖加速碎形影像壓縮 27
5.4 實驗結果 29
5.4.1 FE-SOM及SE-SOM之分群實驗結果 29
5.4.2 使用自組織映射圖於碎形影像壓縮之實驗結果 31
第6章 結論 36
參考文獻 37

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