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研究生:許凱傑
研究生(外文):Hsu, Kai-Chieh
論文名稱:一個植基於RGB立方體模型的高效能與高效率影像壓縮技術
論文名稱(外文):A Novel Effective and Efficient Color Image Compression Technique Based on RGB Cube Model
指導教授:蔡正發蔡正發引用關係
指導教授(外文):Tsai, Cheng-Fa
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系所
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:2013
畢業學年度:101
語文別:中文
論文頁數:99
中文關鍵詞:影像壓縮向量量化網格式分群
外文關鍵詞:Image compressionvector quantizationgrid-based clustering
相關次數:
  • 被引用被引用:2
  • 點閱點閱:99
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  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出一個植基於RGB立方體模型的高效能與高效率影像壓縮技術,此方法可以有效率的找出優良代表色且同時維持高品質的影像壓縮技術,利用RGB立方體模型將原始影像進行色彩分佈切割,並透過編碼簿(Codebook Size)大小、RGB立體模型之子大小及過濾參數以有效率地找尋代表原始影像的代表色。而有別於其他演算法在參數設定上之困難,它解決了使用者參數設定的問題,並針對RGB立體模型之子大小及過濾參數依照使用者欲壓縮之編碼簿大小參數做自動運算的功能,故使用者除了設定編碼簿大小外,無須另外設定參數。
本文所提出之HKC演算法擁有以下優點:(1)HKC僅需輸入一編碼簿大小參數,而其餘內部參數有RGB立方體之單位大小(Cube Size)、過濾門檻值(Filter) 均由本研究整理之公式計算而得。(2)因使用RGB立方體模型之概念故具有穩定且較高的壓縮品質。(3)承(2)因使用RGB立方體模型之概念,在時間成本上有顯著的提升。(4)相較於著名的國際已知影像壓縮技術如LBG、SOM、LazySOM在時間與品質上均有明顯的提升。

This thesis presents a novel effective and efficient color image compression technique based on RGB cube model. This method can efficiently identify good representative color and maintain a high-quality image compression technique. This work includes the following critical steps to perform the HKC image compression: (1)Segment the RGB three-dimensional model with diced size from codebook. (2)Filter parameters to efficiently discover represents color of the original image. The work uses RGB cube model to distributely segment the original image color. Moreover, it utilizes three parameters involving codebook size, the dice size of RGB cube model and filter parameters to efficiently find representatives of the original image colors. To overcome the difficulties of parameter settings for users, this thesis provides automatic function to solve this problem. Therefore, the users do not need to set other parameters for codebook.
The thesis has the merits as follows: (1)This thesis can reduce the color image compression execution time, since it is a RGB cube model technique. (2)This work utilizes RGB cube model concept to increase fairly good color image compression quality (with excellent peak signal-to-noise ratio; PSNR). (3)This work is simple and easily to implement. It needs merely to set a parameter (only codebook size).

摘要 I
Abstract II
謝誌 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究流程 3
1.3 研究範圍與限制 4
1.4 論文架構 5
第二章 文獻探討 6
2.1 彩色影像介紹 6
2.2 向量量化原理 8
2.3 向量量化技術 9
2.3.1 LBG演算法 9
2.3.2 Self-Organizing Map演算法 11
2.3.3 LasySOM演算法 14
2.4 壓縮品質評估方式 16
第三章 研究方法 17
3.1思考流程 17
3.2 HKC演算法 19
3.2.1 RGB立方體概念 19
3.2.2流行色取樣概念 24
3.2.3 HKC演算法詳細步驟 25
3.2.4以Lena影像編碼簿128為例之流程介紹與分析 27
3.2.5 HKC演算法虛擬碼 31
第四章 實驗結果與分析 35
4.1實驗參數 36
4.2實驗環境 36
4.3實驗影像介紹 37
4.4實驗結果 38
4.4.1 Airplane影像之測試實驗 38
4.4.2 Baboon影像之測試實驗 44
4.4.3 Lena影像之測試實驗 50
4.4.4 Sailboat on lake影像之測試實驗 56
4.4.5 Splash影像之測試實驗 62
4.4.6 Tiffany影像之測試實驗 68
4.4.7 Piano影像之測試實驗 74
4.4.8 Friend影像之測試實驗 80
4.4.9 Scene影像之測試實驗 86
第五章 結論與未來展望 92
5.1 結論 92
5.2 未來展望 94
參考文獻 97
作者簡介 99

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[2] Barbalho, J.M.; Costa, J.A.F.; Neto, A.D.D.; Netto, M.L.A.,. “Hierarchical and dynamic SOM applied to image compression,” Proceedings of the International Joint Conference on Neural Networks,.2003, Vol. 1, pp. 753-758, 2003.
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[7] Kohonen, T., “Self-organizing map,” Springer, Berlim, 1995.
[8] Kanjanawanishkul, K., Uyyanonvara, B., “Novel fast color reduction algorithm for time-constrained applications,” Journal of Visual Communication and Image Representation, Vol.16, pp. 311-332, 2005.
[9] Kurdthongmee, W., “A Colour Image Quantization Algorithm for Time-Constrained Applications,” Walailak Journal of Science and Technology, Vol. 2, pp. 149-168, 2005.
[10] Linde, Y., Buzo, A., Gray, R.M., “An algorithm for vector quantizationdesign,” IEEE Transaction Commun., Vol. COM-28, pp. 84-95, 1980.
[11] Rasti, J., Monadjemi A., Vafaei, A., “Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features,” Expert Systems with Applications, Vol. 38, No. 10, pp. 13188-13197, 2011.
[12] Tsai, C.F., Lin, Y.J., “LazySOM: Image Compression Using An Enhanced Self-Organizing Map,” Lecture Notes in Computer Science, Vol. 5414, pp. 118-129, 2009.
[13] Tsai, C.F., Lin, Y.J., “LISA: Image Compression Scheme.Based on An Asymmetric Hierarchical Self-Organizing Map,” Lecture.Notes in Computer Science, Vol. 5553, pp. 476-485, 2009.
[14] Tsai, C.F., Ju, J.H., “ELSA:A New Image Compression.Using An Expanding-Leaf Segmentation Algorithm,” Lecture Notes in .Computer Science, Vol. 5579, pp. 624-633, 2009.
[15] Tsai, C.F., Ju, J.H., “INTSOM: Gray Image .Compression Using An Intelligent Self-Organizing Map,” Studies in .Computational Intelligence, Vol. 199, pp. 31-40, 2009.
[16] Tsai, C.F., Lin, Y.S., Wang, J.C., “SEAN: A Simple .expanding-tree algorithm based on mean-division for color quantization,” proceedings of International Conference on Machine Learning and Cybernetics, pp. 2658-2663, 2010.

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