博碩士論文 100521072 詳細資訊




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姓名 李宗勳(Tsung-Shun Lee)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 改良式粒子群方法之無失真影像預測編碼應用
(Predictive Coding for Lossless Image Compression Based on Improved PSO)
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摘要(中) 本論文中,我們提出了一種改良式的粒子群演算法,名為增減式慣性權重粒子群演算法(particle swarm optimization with increasing - decreasing inertia weights, IDWPSO),並應用IDWPSO於影像壓縮。標準粒子群演算法中每一個體使用共同的慣性權重,而本文所提的方法,使粒子能自適應性地產生自己的慣性權重,在粒子群最佳化初期,透過遞增慣性權重,可以更有效的從局部探索開始,逐漸演化,等到集中收斂至一階段後,再將其它轉換成二階遞減模式以求迅速的把其它個體帶往全域最佳解。接著我們處理影像壓縮的問題,利用IDWPSO方法,獲取更好的壓縮率。由於我們得知最小平方法產生的預測誤差,往往出現在影像邊界相交處,所以我們在偵測到影像邊界時,採取IDWPSO預測器來提升預測的精確性,以防止耗費大量的運算,減少系統的運算的複雜度。從實驗結果證實,所提出的IDWPSO可以大幅的增進預測的精確性,最後在位元率(bit/pixel)的比較方面,與MED (Median Edge Detector, MED)相比改善了約7%、與GAP (Gradient-Adjusted Prediction, GAP)相比改進了約4%,也比EDP (Edge-directed Prediction, EDP)相比降低了約2%,證實所提出的演算法的確能有效提高影像編碼的效能。
摘要(英) In this thesis, we propose a modified optimization algorithm which is called particle swarm optimization with increasing-decreasing inertia weights (IDWPSO). Unlike the standard PSO algorithm, the proposed IDWPSO utilizes different weights for different particles. Initially, a small inertia weight is used for each particle to begin a global search. Then the individual inertia weights are respectively increasing linearly for more effective local searches. Finally, the inertia weights are switched to a larger value and then decreased quadratically to find a convergent optimum. Afterwards, the IDWPSO is applied to image coding problem as an image predictor. The IDWPSO predictor will be operated only when an edge is detected. The experimental results show that the proposed lossless image coding approach obtains more accurate image prediction. And better bit-rate compression is also obtained. As seen in the experiments, the IDWPSO is a 7% improvement over the MED (Median Edge Detector, MED), 4% over GAP (Gradient-Adjusted Prediction, GAP), and 2% over EDP (Edge-directed Prediction, EDP). These demonstrate the effectiveness of the proposed IDWPSO for the image coding.
關鍵字(中) ★ 無失真影像壓縮架構
★ 最佳化方法
★ 粒子群演算法
★ 預測誤差補償
★ 算術編碼器
關鍵字(英) ★ Lossless image compression architecture
★ Optimization Methods
★ Particle Swarm Optimization
★ Error Compensation Mechanism
★ Arithmetic Coding
論文目次 目錄
摘要 I
Abstract II
圖目錄 VII
表目錄 X
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究貢獻及方式 1
1.3 本論文流程架構 3
第二章 文獻探討 4
2.1 背景回顧 4
2.2.1 失真壓縮 4
2.1.1.1 Vector Quantization (VQ) 4
2.1.1.2 Joint Photographic Experts Group (JPEG) 7
2.2.2 近乎無失真壓縮 10
2.2.3 無失真壓縮 11
2.2 編碼制度 11
2.2.4 一回合制編碼 12
2.2.5 多回合制編碼 12
2.3 條件誤差熵值(Conditional Entropy Coding) 13
2.4 無失真演算法的預測器架構 13
2.4.1 Median Edge Detector (MED) 14
2.4.2 Gradient-Adjusted Prediction (GAP) 16
2.4.3 Edge-Directed Prediction (EDP) 18
第三章 提出改良式粒子群最佳化方法暨模擬 23
3.1 粒子群演算法背景與流程 23
3.1.1 粒子群演算法背景介紹 (PSO) 23
3.1.2 標準粒子群演算法架構 24
3.2 PSO改進策略 25
3.2.1 切換式增減慣性權重 25
3.2.2 協作式機制 29
3.3 模擬實驗結果 30
3.3.1 10維測試結果 36
3.3.2 30維測試結果 43
第四章 實現改良式粒子群演算法於無失真影像壓縮之預測架構 51
4.1 二元模式 (Binary Mode) 53
4.2 邊界偵測 (Edge detector) 55
4.3 IDWPSO預測器 57
4.3.1 IDWPSO預測器-粒子初始化 57
4.3.2 IDWPSO預測器-慣性權重切換調整 58
4.3.3 IDWPSO預測器-粒子訊息互換 59
4.3.4 IDWPSO預測器-粒子適應度的計算 61
4.3.5 IDWPSO預測器-粒子群演算法所使用參數 62
4.4 預測誤差補償機制 63
4.4.1 預測誤差補償的模組建立 63
4.4.1.1 紋理模型 (Texture Context Model) 63
4.4.1.2 預測誤差模型 (Error Context Model) 64
4.4.2 PSO預測誤差的量化方式 65
4.4.3 預測誤差的補償 67
4.4.4 預測誤差的集中分佈方法 69
4.4.4.1 預測誤差映射方法 69
4.4.4.2 反轉預測誤差方法 (Error Sign Flipping) 71
4.4.5 算術編碼器 74
第五章 改良式粒子群演算法於無失真影像壓縮之預測架構實驗結果分析 76
5.1 演算法參數分析 76
5.1.1 演化週期 (Period) 76
5.1.2 慣性權重 (Inertia weight) 78
5.1.3 加速因子 (Accelerate of factor) 79
5.1.4邊界偵測器權重 (Edge detection weight) 80
5.2 改良式粒子群演算法與(IDWPSO)暨各類預測器模擬 82
5.2.1 IDWPSO預測器與SPSO預測器比較結果 82
5.2.2 IDWPSO預測器與現行預測器比較結果 84
5.3 改良式粒子群演算法與(IDWPSO)複雜度分析 86
5.3.1 IDWPSO預測器複雜度分析 86
5.3.2 EDP預測器複雜度分析 87
5.3.3 分析結論 88
第六章 論文總結與未來發展 89
6.1 論文總結 89
6.2未來發展 90
參考文獻 91
附錄A 95
參考文獻 [1] Qiang Huo, and Chorkin Chan. ”Contextual vector quantization for speech recognition with discrete hidden Markov model,” Pattern Recognition, 1995, Vol. 28, pp. 513 – 517.
[2] Linde, Y., Buzo, A. Gray, R.M. “An Algorithm for Vector Quantizer Design,” IEEE Transactions on Communications, 1980 , Vol. 28 , pp. 84 – 94.
[3] Marcelo J. Weinberger, Gadiel Seroussi, and Guillermo Sapiro. “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Transactions on Image Processing, 2000 , Vol. 9, No.8 , pp. 1309 – 1324.
[4] Xin Li, and Michael T. Orchard, “Edge directed prediction for lossless compression of natural images,” IEEE Transactions on Image Processing, 2001 , Vol. 10, No.6 , pp. 813 – 817.
[5] Giovanni Motta, James A. Storer, and Bruno Capentieri. “Lossless image coding via adaptive linear prediction and classification,” Proceeding of the IEEE, 2000, Vol. 88, No.11, pp. 1790 – 1796.
[6] H Jingsong, W Xufa, Z Min, W Jiying, F Qiansheng. “New research on scalability of lossless image compression by GP engine,” in Proc. 2005 NASA/DoD Conf. Evolvable Hardware, 2005 , pp. 160 – 164.
[7] B Meyer,and P.E Tischer. ” TMW-a New Method for Lossless Image Compression,” in Proc. Int. Picture Coding Symp., Berlin, Germany, Oct. 1997 [online]. Available: http://www.csse.monash.edu.au/~bmeyer/tmw/paper.ps
[8] K Sayood. “introduction to data compression, 3rd ed.,” New York: Mogan-Kaufmann, 2006, pp. 139 – 163.
[9] X. Wu, and N. Memon. “Context-Base, Adaptive, Lossless Image Coding,” IEEE Transactions on Communications, 1997 , Vol. 45, No.4, pp. 437 – 444.
[10] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, “Numerical Recipes in C,” Cambrige, U.K.: Cambtige University Press, 2002 , pp. 96 – 98 [online].Available: http://www.library.cornell.edu/nr/cbookcpdf.html.
[11] Lih-Jen Kau, and Yuan-Pei Lin. “Least-Square-Based Switching Structure for Lossless Image Coding,” IEEE Transactions Circuits and System-I: Regular Papers, 2007 , Vol. 54, No.7, pp. 1529 – 1541.
[12] J. Kennedy, and R. C. Eberhart. “Particle swarm optimization,”. Proceedings IEEE International Conference on Neural Networks,1995 ,Vol. 4 , pp. 1942 – 1948.
[13] J. Kennedy, and R.C. Eberhart. “A discrete binary version of the particle swarm algorithm,”. IEEE International Conference on Systems, 1997 , Vol. 5 , pp. 4104 – 4108.
[14] Y. Shi, and R. C. Eberhart. “Fuzzy adaptive particle swarm optimization,”. Proceeding of the 2001 Congress on Evolutionary Computation, 2001 , Vol. 1 , pp. 101 – 106.
[15] 郭信川, 張建仁, 劉清祥 . 粒子群演算法於最優化問題之研究. 第一屆台灣作業研究會學術研討會暨2004年科技與管理學術研討會, 2004 , pp. 419 – 432.
[16] K. Premalatha, A.M. Natarajan. “Hybrid PSO and GA for Global Maximization,”. Int. J. Open Problems Compt. Math., 2009 , Vol. 2 , pp. 597 – 608.
[17] Y. Shi and R.C. Eberhart. “Parameter selection in particle swarm optimization,”. in Proc. 7th Int. Conf. Evol. Program., 1998 , Vol. 1447 , pp. 591 – 600.
[18]Y. Shi and R.C. Eberhart, “A modified particle swarm optimizer,”. in Proc. IEEE Int. Conf. Evol. Comput, 1998, pp. 69 – 73.
[19] Y. Shi and R.C. Eberhart . “Empirical study of particle swarm optimization,”. in Proc. IEEE Congr. Evol. Comput, 1999, Vol. 3 , pp. 1945 – 1950.
[20] Y. -L. Zheng, L. -H. Ma, L. -Y. Zhang et al. “On the convergence analysis and parameter selection in particle swarm optimization,”. in Proc. IEEE Int. Conf. Mach. Learning Cybern, 2003 , Vol. 3 , pp. 1802 – 1807.
[21] Y. -L. Zheng, L. -H. Ma, L. -Y. Zhang et al. “Empirical study of particle swarm optimizer with an increasing inertia weight”. in Proc. IEEE Congr. Evol. Comput, 2003 , Vol. 1 , pp. 221 – 226.
[22] V.D.B.Frans, P.E.Andries. “A cooperative approach to particle swarm optimization,”. IEEE Trans. on Evolutionary Computation, 2004 , Vol. 8 , pp. 225 – 239.
[23] Shen huisheng and Zhao yanmin. “An Improved cooperative PSO Algorithm,”. in International Conference on Mechatronic Science, Electric Engineering and Computer, 2011 , pp. 1040 – 1042.
[24] R. Salomon. “Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions,”. BioSystems, 1996 , pp. 263 – 278.
[25] K. Tang, Xiaodong Li, P. N. Suganthan, Z. Yang and T. Weise. “Benchmark Functions for the CECˈ2010 Special Session and Competition on Large-Scale Global Optimization,” Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, http://nical.ustc.edu.cn/cec10ss.php, 2009
[26] M. Pant, T. Radha, V.P.Singh. “A new particle swarm optimization with quadratic interpolation,” In Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications,2007 , Vol. 1 , pp. 55 – 60.
[27] K.E. Parsopoulos , M.N. Vrahatis. “UPSO: a unified particle swarm scheme,”. In Lecture series on Computer and Computational Sciences, 2004 , pp. 868 – 873.
[28] R. Mendes, J. Kennedy , J. Neves. “The fully informed particle swarm: simpler, maybe better,” IEEE Transactions on Evolutionary Computation, 2004 , Vol. 8 , pp. 204–210.
[29] J.J. Liang , P.N. Suganthan. “Dynamic multi-swarm particle swarm optimizer,” In Proceedings of IEEE on Swarm Intelligence Symposium, 2005 , pp. 124 – 129.
[30] J.J. Liang , A.K. Qin , P.N Suganthan and S. Baskar. “Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions,”. IEEE Transactions on Evolutionary Computation, 2006 , Vol. 10 , pp. 281 – 296.
[31] M. Pant, T. Radha, and V. P. Singh, “A new particle swarm optimization whit quadeatic interpolation,” In Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications, 2007, pp. 55 – 60
[32] Y. -T Juang , S. -L. Tung , H. -C. Chiu. “Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” International Journal of Information Sciences, 2010. [Online]. Available: http://dx.doi.org/10.1016/j.ins.2010.11.025.
[33] 方惠珊,「肺腺癌細胞之電腦輔助圖形辨識」, 國立雲林科技大學, 碩士論文, 民國96年。
[34] Lih-Jen Kau, and Chih –Shen Chen, “A Low Complexity Dual Mode Edge Detector,” in Proc. IEEE Visual Communication and Image Processing (VCIP 2011), Tainan, Taiwan, Nov.6-9, 2011.
[35] Xiaolin Wu, “Lossless Compression of Continuous-Tone Images via Context Selection, Quantization, and Modeling,” IEEE Transaction on Image Processing, 1997, Vol.6, No.5, pp. 656 – 664.
指導教授 莊堯棠(Y.-T. Juang) 審核日期 2013-7-9
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