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研究生:陳立恆
研究生(外文):Li-Heng Chen
論文名稱:基於機器學習方法之影像品質鑑定模型
論文名稱(外文):Machine Learning Based Image Quality Assessment Model
指導教授:貝蘇章
口試委員:杭學鳴鍾國亮林康平
口試日期:2014-05-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
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:2014
畢業學年度:102
語文別:英文
論文頁數:106
中文關鍵詞:人眼視覺系統影像品質評估結構相似性支持向量機
外文關鍵詞:Full reference image quality assessment (IQA)support vector regression (SVR)human visual system (HVS)retinadifference of Gaussian (DOG)color distortionPSNRSSIMFSIM
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如何準確的評估影像的品質在影像處理的研究及應用扮演著舉足輕重的腳色,傳統測量影像品質的方法是比較原始影像與失真影像的峰值信噪比(PSNR),但近年來許多研究發現PSNR所測量出來的影像失真度和人眼視覺系統(HVS)有所不符,換句話說,我們使用的PSNR所測量出來的影像品質好壞和人眼所感知的影像品質是不一致的,因此我們需要發展新的影像品質測量演算法來解決這個問題。
首先我們會以一些淺顯易懂的例子來說明PSNR不符合HVS的事實,接著會介紹一些其他人提出的方法,如SSIM, FSIM, MSVD等。最後提出我們利用機器學習的方法結合已存在的不同頻帶的影像品質特徵和色彩特徵,訓練出和人眼視覺系統有高度相關性的模型。此外,我們也試著以Saliency map 來改善現有的影像品質估測方法,對此我們也做了一系列的實驗。


The objective image quality assessment (IQA) plays a key role in the development of various multimedia applications. The release of new IQA dataset (TID2013) challenges the wildly used 2D IQA metrics (e.g. PSNR and SSIM) since they cannot handle the diversity of distortion types. In this thesis, we propose a machine learning approach IQA model with features extracted from different frequency band (DOG features). The color distortion is also considered in our system. The effectiveness of our IQA system is verified by comparing with subjective score on the available databases. The experimental results show the high consistency between MOS score and our metric.

口試委員會審定書 ........................................................................................................... #
誌謝 .................................................................................................................................. ii
中文摘要 ..........................................................................................................................iv
ABSTRACT .....................................................................................................................vi
CONTENTS .................................................................................................................. viii
LIST OF FIGURES ........................................................................................................ xii
LIST OF TABLES ..........................................................................................................xvi
Chapter 1 Introduction .............................................................................................. 1
Chapter 2 Related Image Quality Assessment......................................................... 3
2.1 Introduction..................................................................................................... 3
2.1.1 Subjective score and Objective score .................................................... 3
2.1.2 Performance evaluation ......................................................................... 5
2.2 Image quality assessment in mean square error sense .................................. 10
2.2.1 Peak signal to noise ratio (PSNR) ....................................................... 11
2.2.2 Problems of the mean square error based image quality assessment .. 12
2.3 Structure similarity image quality assessment (SSIM) ................................. 17
2.3.1 Introduction to structure similarity index ............................................ 17
2.3.2 Properties of SSIM index .................................................................... 20
2.3.3 Image quality assessment using SSIM index ...................................... 20
2.3.4 Comparison and experimental results ................................................. 23
2.3.5 Other applications of SSIM ................................................................. 25
2.3.6 Multi-scale structural similarity .......................................................... 26
2.4 SVD based image quality assessment........................................................... 28
2.4.1 Singular value decomposition ............................................................. 28
2.4.2 Image quality metric with singular values (M-SVD) .......................... 30
2.4.3 Image quality metric with singular vectors (V-SVD) ......................... 32
2.5 Feature similarity image quality assessment ................................................ 36
2.5.1 Structure information of FSIM index .................................................. 36
2.5.2 Framework of FSIM metric ................................................................ 41
Chapter 3 Machine learning based IQA model ..................................................... 45
3.1 Machine learning based image quality assessment ...................................... 45
3.1.1 System overview ................................................................................. 45
3.1.2 Features for image quality assessment ................................................ 46
3.1.3 Support Vector Regression .................................................................. 52
3.1.4 Implementation details ........................................................................ 53
3.1.5 Experimental results on TID2013 database ........................................ 60
3.1.6 Experimental results on other databases ............................................. 64
3.2 Image quality metric with visual saliency consideration .............................. 66
3.2.1 Saliency map ....................................................................................... 66
3.2.2 Visual saliency considered image quality metrics............................... 68
Chapter 4 IQA metric model with multi band extracted features ....................... 75
4.1 Difference of Gaussian ................................................................................. 75
4.1.1 Difference of Gaussian ........................................................................ 75
4.1.2 Multi band decomposition by DOG .................................................... 76
4.1.3 Perfect reconstruction in DOG decomposition ................................... 79
4.2 Feature extraction ......................................................................................... 80
4.2.1 Multi band feature extraction .............................................................. 80
4.2.2 Feature extraction with mixture metric module .................................. 85
4.3 Experimental results ..................................................................................... 85
4.3.1 Experimental results on TID2013 database ........................................ 86
4.3.2 Experimental results on other databases (LIVE and CSIQ) ................ 89
4.3.3 Experimental results on color distorted subset (TID2013) ................. 97
Chapter 5 Conclusion and future work ............................................................... 101
5.1 Conclusion .................................................................................................. 101
5.2 Future work ................................................................................................. 101
REFERENCE ................................................................................................................ 103

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