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研究生:張文昕
研究生(外文):Wen-Hsin Chang
論文名稱:基於渾沌進化演算論之JPEG2000小波分解最佳化設計
論文名稱(外文):JPEG 2000 Wavelet Filter Design Framework with Chaos Evolutionary Programming
指導教授:郭淑美郭淑美引用關係
指導教授(外文):Shu-Mei Guo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:87
中文關鍵詞:小波轉換失真壓縮資料壓縮
外文關鍵詞:Image compressionJPEG 2000Discrete wavelet transformChaosEvolutionary programming
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以小波轉換為基礎的資料壓縮方式為目前越來越熱門的課題,利用離散小波轉換Discrete Wavelet Transform (DWT)為基礎的壓縮技巧如 Embedded Zerotree Wavelet encoder (EZW)、Set Partitioning in Hierarchical (SPIHT),其壓縮效率皆能在高壓縮率的狀況下超越原本以離散小波轉換Discrete Cosine Transform (DCT)為主的的壓縮技術。
小波轉換的資料壓縮標準中,JPEG 2000 為目前最先進的靜態影像壓縮技術,J2K使用Daubenchies 9/7作為失真壓縮的小波轉換標準。小波轉換的種類相當繁多,研究指出,各種不同的小波轉換在每張影像有不同的壓縮效果,換言之,針對每張影像可以嘗試找出其最適合的小波轉換。
本篇論文提供了一個最佳化的機制與構想,可以提升JPEG 2000與其他小波轉換的資料壓縮標準的影像品質,只要稍微修改壓縮封包的檔頭,未來可以在各種標準下實現更多不同需求、針對不同長度的小波分解做最佳化。研究結果也指出CEP可以有效的提升每個壓縮率下的影像品質,CEP最佳化的過程為了尋找最適合的小波分解係數需要花點時間,但是每一張影像只需要做一次便可隨時重現研究成果,解碼端使用者若接到CEP最佳化過的影像格式,即可迅速、確實的獲得影像品質的提升。
JPEG 2000 (J2K) is an international standard for still image compression, which is based on wavelet transformation and adopts the Daubenchies 9/7 filter for lossy compression. Considering that a wavelet filter might be suitable for one image but not for the other in regard to reconstruction quality, in this thesis, we propose a novel filter design framework based on the Daubenchies 9/7 filter, which employs chaos evolution programming (CEP) to optimize the wavelet filter for each specific image. The customized filter design is ready to incorporate into the J2K codec since the filter coefficients can be constructed with only one single parameter, which can be easily packaged into the J2K header. Experimental results show that CEP-trained filters achieve higher image quality.
Abstract II
List of Figure VI
List of Table VII
Chapter 1 Introduction 1
Chapter 2 JPEG2000 2
2.1 Tiling 3
2.2 DC level shifting 4
2.3 Multicomponent transform 4
2.3.1 Reversible color transformation 5
2.3.2 Irreversible color transformation 5
2.4 Discrete wavelet transform 6
2.4.1 What are basis functions 6
2.4.2 Wavelet and Fourier transform 7
2.4.3 Analysis 9
2.4.3.1 The discrete wavelet transform 9
2.4.3.2 The fast wavelet transform 10
2.4.3.3 Wavelet package 10
2.4.4 Wavelet coefficients 11
2.4.4.1 Scaling and wavelet coefficients 11
2.4.4.2 Two-channel filter bank/PR filter 14
2.4.4.3 Biorthogonal filters 21
2.4.4.4 Lifting scheme 23
2.4.4.5 The derivation of tuning parameter 27
2.5 Quantization 28
2.6 Entropy coding 29
2.6.1 Partitioning data for coding 30
2.6.2 Embedded block coding with optimized truncation 30
2.6.3 Fractional BPC coding 31
2.6.4 Terminology 31
2.6.5 Coding operations 34
2.6.6 Coding passes 38
2.6.7 Example of BPC coding 43
2.7 Arithmetic entropy coding 50
2.7.1 General arithmetic coder 50
2.7.2 Binary arithmetic coder 55
2.7.3 MQ-coder 59
2.7.4 MQ-coder implementation 61
2.8 Package and layer formation 67
Chapter 3 The Novel Chaos Evolutionary Programming Algorithm 69
3.1 Evolutionary programming (EP) 69
3.2 Chaos 69
3.3 Chaos evolutionary programming algorithm 70
Chapter 4 Experimental Result 78
4.1 Experiment simulation 78
4.2 Simulation result 78
4.3 Wavelet property 81
Chapter 5 Future Work and Conclusion 84
Reference 85
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