臺灣博碩士論文加值系統

在碎形影像壓縮研究領域中，縮短壓縮時間長久以來一直是個重要的議題。本篇論文提出一種植基於區塊對比型態的分類方法。依照區塊的對比型態，將區塊分為平滑、棋盤狀、水平、垂直、對角、反對角，共六類。讓range block只搜尋相關的domain block，因此得以利用紋理資訊來達到縮短壓縮時間的目的。
此外結合空間關聯性的作法，讓大部分range block在周邊搜尋時便能找到合適的domain block。針對少部份無法在周邊搜尋找到合適對象的range block，再作全域紋理搜尋。實驗數據顯示，利用對比型態分類法可以有效縮短壓縮時間，並讓影像品質維持在一定的水準之上。

Speeding up fractal image encoding is an important issue. In this thesis, we propose a fractal encoding method based on contrast-pattern classification. According to contrast-pattern of each block, range and domain blocks are divided into 6 classes – Smooth, Chessboard, Horizontal, Vertical, Diagonal and Anti-diagonal (SCHVD). Then, for each range block, we only search domain blocks in the corresponding domain pool to speed up fractal encoding. By limiting the domain pool for each range block, the encoding time can be shortened.
Moreover, the proposed method can be combined with the spatial-correlation method. In this hybrid approach, most range blocks could get qualified domain blocks from neighbor’s fractal code. For others, which can’t get domain blocks from neighbor search, the SCHVD classification is served to limit the size of domain pool to speed up encoding. Experiment results show that the proposed algorithm can shorten encoding time and also keep the quality of the reconstructed image.

摘要 I
Abstract II
目錄 III
圖表目錄 IV
1. 緒論 1
2. 碎形影像壓縮 2
2.1. Partition Iterated Function System (PIFS) 2
2.2. Baseline碎形影像壓縮 3
3. 對比型態分類法 5
3.1. “負片” 特性 5
3.2. 紋理分類(SCHVD) 6
3.3. Cross-class reference 10
3.4. 高變異的Range Block 12
4. 結合空間關聯性 14
4.1. 先前研究:利用空間關聯性 14
4.2. SCHVD與空間關聯性結合 15
5. 實驗結果 17
5.1. 與Baseline方法比較 17
5.2. 與DCT分類法比較(Exhaustive) 19
5.3. 與DCT分類法比較(Fixed Domain Number) 22
5.4. 針對高變異range block的改善 24
5.5. 與空間關聯性結合 25
6. 結論 26
參考文獻 27

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