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| 研究生： |
康哲誌 CHE-CHIH KAN |
|---|---|
| 論文名稱： |
植基於對比型態分類的快速碎形影像壓縮法 Fast fractal image encoding based on contrast pattern classification |
| 指導教授： |
鍾國亮
Kuo-Liang Chung |
| 口試委員: |
傅楸善
none 陳秀娘 none 古鴻炎 Hung-Yan Gu 洪西進 Shi-Jinn Horng |
| 學位類別： |
碩士 Master |
| 系所名稱： |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
| 論文出版年： | 2008 |
| 畢業學年度： | 96 |
| 語文別： | 中文 |
| 論文頁數： | 28 |
| 中文關鍵詞： | 碎形影像壓縮 、對比分類法 、SCHVD 、紋理分析 、空間關聯性 |
| 外文關鍵詞： | Fractal image encoding, Contrast-pattern classification, SCHVD, Texture analysis, Spatial-correlation |
| 相關次數： | 點閱：165 下載：1 |
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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.
[1]Barnsley MF, Fractal everywhere. New York: Academic; 1988.
[2]Jacquin AE, Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans Image Process 1992, 1, 18–30.
[3]Wohlberg B, De Jager G, A review of the fractal image coding literature. IEEE Trans Image Process 1999, 12, 1716–29.
[4]Fisher Y, editor. Fractal image compression: theory and application. New York: Springer-Verlag; 1994.
[5]Truong T.K., Jeng J.H., Reed I.S., Lee P.C., Li A.Q., A fast encoding algorithm for fractal image compression using the DCT inner product. IEEE Trans Image Process 2000, 4, 529–35.
[6]Lai CM, Lam KM, Siu WC., A fast fractal image coding based on kick-out and zero contrast conditions. IEEE Trans Image Process 2003, 11, 1398–403.
[7]He C., Xu X., Yang J., Fast fractal image encoding using one-norm of normalised block (2006) Chaos, Solitons and Fractals, 27 (5), 1178–1186.
[8]Lee C.K., and Lee W.K., Fast fractal image block coding based on local variances, IEEE Trans. Image Process., 1998, 7, (6), 888–891.
[9]He C., Yang S.X., Huang X., Variance-based accelerating scheme for fractal image encoding. Electron Lett, 2004, 2, 115–6.
[10]Ghosh SK, Mukherjee J, Das PP., Fractal image compression: a randomized approach. Pattern Recognition Lett, 2004, 25, 1013–24.
[11]Truong T.K., Kung C.M., Jeng J.H., Hsieh M.L., Fast fractal image compression using spatial correlation. Chaos, Solitons & Fractals 2004, 22, 1071–6.
[12]Furao S., Hasegawa O., A fast no search fractal image coding method, Signal Processing: Image Communication, 2004, 19 (5), 393-404.
[13]Duh D.J., Jeng J.H., Chen S.Y., DCT based simple classification scheme for fractal image compression, Image and Vision Computing, 2005, 23, 1115-21.
[14]Chung K.L., Hsu C.H., Novel prediction- and subblock-based algorithm for fractal image compression, Chaos, Solitons and Fractals, 2006, 29 (1), 215-222.
