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研究生:林建勝
研究生(外文):Chien-sheng Lin
論文名稱:經由視覺特性最佳化之JPEG2000編碼器
論文名稱(外文):A Perceptually Optimized JPEG2000 Encoder
指導教授:周俊賢周俊賢引用關係
指導教授(外文):Chun-hsien Chou
學位類別:碩士
校院名稱:大同大學
系所名稱:電機工程學系(所)
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:2004
畢業學年度:93
語文別:英文
論文頁數:70
中文關鍵詞:視覺特性 最佳化
外文關鍵詞:JPEG2000 optimize perceptual
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隨著在有限頻寬媒介上傳輸多媒體視覺性資料的需求不斷地增加,有越來越多的研究是利用人類視覺模型來強化壓縮技術,並且能得到視覺品質不錯的壓縮影像。JPEG2000是一種符合ISO/ITU標準的新靜態影像壓縮技術,由其多重解析(multi-resolution)小波分解及雙層式(two-tier)編碼架構非常適合將人類視覺模型加入其編碼演算法中,但是就本質上來說,JPEG2000編碼器是一種以位元率為目標考量而將壓縮所造成的失真量最小化的演算法。透過該演算法壓縮影像,內容不同之影像在相同位元率要求的編碼環境下會造成不同的視覺品質。因此,本篇論文將重點放在JPEG2000編碼效益的提升,透過編碼過程中視覺冗贅訊號的移除,使得低位元率彩色影像編碼具有相同的視覺品質。同時,考慮人類視覺對亮度及彩度訊號因不同空間頻率而產生的靈敏度差異,將每一色頻的全頻帶臨界可視失真面分解成不同次頻帶的臨界可視失真面組合。透過每一次頻帶的臨界可視失真面所賦予之臨界視覺失真值,三個色頻上所有視覺不重要小波係數將優先被全數移除而不需要編碼。在不改變JPEG2000壓縮位元串流格式的情況下,原先符合位元率與均方根誤差失真成反比定理的編碼器,可被修正成位元率與可視失真成反比。跟傳統的JPEG2000作比較,我們的方法的確能夠有效地移除視覺冗贅訊號,也提供了在低位元率下有更佳的視覺品質。
Driven by a growing demand for transmission of visual data over media with limited capacity, increasing efforts have been made to strengthen compression techniques and maintain good visual quality of the compressed image by human visual model. JPEG2000 is the new ISO/ITU standard for still image compression. The multi-resolution wavelet decomposition and the two-tier coding structure of JPEG2000 make it suitable for incorporating the human visual model into the coding algorithm, but the JPEG2000 coder is intrinsically a rate-based distortion minimization algorithm, by which different images coded at the same bit rate always result in different visual qualities. The research will focus on enhancing the performance of the JPEG2000 coder by effectively excluding the perceptually redundant signals from the coding process such that color images encoded at low bit rates have consistent visual quality. By considering the varying sensitivities of the human visual perception to luminance and chrominance signals of different spatial frequencies, the full-band JND profile for each color channel will be decomposed into component JND profiles for different wavelet subbands. With error visibility thresholds provided by the JND profile of each subband, the perceptually insignificant wavelet coefficients in three color channels will be first removed. Without altering the format of the compressed bit stream, the encoder is modified in a way that the bit rate is inversely correlated with the perceptible distortion rather than the distortion of mean square errors. As compared to the JPEG2000 standard, the proposed algorithm can remove more perceptual redundancy from the original image, and the visual quality of the reconstructed image is much more acceptable at low rates.
ABSTRACT (in Chinese) Ⅰ
ABSTRACT (in English) Ⅱ
ACKNOWLEGMENT Ⅲ
CONTENTS Ⅳ
LIST OF FIGURES Ⅵ
LIST OF TABLES Ⅷ
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Perceptual Coding 3
1.3 Objective 4
1.4 Approaches of the Proposed Coding Algorithm 5
1.5 Organization of this Thesis 5
CHAPTER 2 BREF DESCRIPTION OF THE JPEG-LS ALGORITHM 6
2.1 Basic Structure of the JPEG2000 Encoder 6
2.2 Tile and Color Component Transform 7
2.3 Discrete Wavelet Transform 9
2.4 Coefficient Bit Model 13
2.5 Arithmetic Coding 20
2.6 Rate control 21
CHAPTER 3 THE PERCEPTUAL MODEL FOR ESTIMATING JND FILES 24
3.1 Basic Structure of the Proposed Perceptual Model 24
3.2 JND Profile Estimation for Luminance Signals 25
3.2.1 Contrast sensitivity 26
3.2.2 Texture masking 28
3.2.3 Spatial JND profile for luminance signals 30
3.3 JND Profile Estimation for Chrominance Signals 32
3.3.1 JNCD in a uniform color space 33
3.3.2 Intercomponent masking effect 35
3.3.3 Spatial JND profiles estimation for chrominance signals 37
CHAPTER 4 THE PROPOSED IMAGE CODEC 45
4.1 Basic Structure of the Proposed Image Encoder 45
4.2 Distortion Allocation of JND Profiles 47
4.2.1 Distortion allocation for luminance signals 49
4.2.2 Distortion allocation for chrominance signals 51
4.3 Perceptual Distortion Evaluation 53
4.4 Perceptually Optimized JPEG2000 Encoder 53
CHAPTER 5 SIMULATION RESULTS 55
5.1 Conditions in Simulation 55
5.2 Fidelity Measure Criterion 56
5.3 Experimental Results 57
CHAPTER 6 CONCLUSIONS 68
REFERENCE 69
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