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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 貝蘇章 | |
| dc.contributor.author | Li-Heng Chen | en |
| dc.contributor.author | 陳立恆 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:07:26Z | - |
| dc.date.available | 2016-07-22 | |
| dc.date.copyright | 2014-07-22 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-06-10 | |
| dc.identifier.citation | [1] Z. Wang and A.C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett., vol. 9, pp. 81-84, 2002.
[2] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004. [3] Z. Wang, E.P. Simoncelli, and A.C. Bovik, “Multi-scale structural similarity for image quality assessment,” ACSSC’03, pp. 1398-1402, 2003. [4] Z. Wang and A. Bovik, “Mean squared error: Love it or leave it? a new look at signal fidelity measures,” Signal Processing Magazine, IEEE, vol. 26, no. 1, pp. 98 –117, jan. 2009. [5] H.R. Sheikh et al., “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Processing, vol. 15, no. 11, pp. 3440-3451, 2006 [6] N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008 - A database for evaluation of full-reference visual quality assessment metrics, ” Advances of Modern Radioelectronics, vol. 10, pp. 30-45, 2009. [7] N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, C.-C. Jay Kuo, “Color Image Database TID2013: Peculiarities and Preliminary Results,” Proceedings of 4th Europian Workshop on Visual Information Processing EUVIP2013, Paris, France, June 10-12, 2013, pp. 106-111. [8] N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, C.-C. Jay Kuo, “A New Color Image Database TID2013: Innovations and Results,” Proceedings of ACIVS, Poznan, Poland, Oct. 2013, pp. 402-413. [9] E.C. Larson and D.M. Chandler, “Most apparent distortion: full-reference image quality assessment and the role of strategy,” J. Electr. Imaging, vol. 19, pp. 001006:1-21, 2010. [10] Z. Wang, “SSIM Index for Image Quality Assessment,” [Online] Available: http://www.ece.uwaterloo.ca/~z70wang/research/ssim/. [11] A. Eskicioglu, A. Gusev, and A. Shnayderman, “An SVD-based grayscale image quality measure for local and global assessment,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 422–429, Feb. 2006. [12] M. Narwaria and W. Lin, “Scalable image quality assessment based on structural vectors,” in Proc. IEEE Int. Workshop MMSP, Rio de Janeiro, Brazil, Oct. 5–7, 2009, pp. 1–6. [13] M. Narwaria, and W. Lin, “Objective image quality assessment based on support vector regression,” IEEE Trans. on Neural Networks, vol. 21, no. 3, pp. 515-519, Mar. 2010. [14] C.C. Chang and C.J. Lin, “LIBSVM : A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. [Online] Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm [15] M.C. Morrone and D.C. Burr, “Feature detection in human vision: a phase-dependent energy model,” Proc. R. Soc. Lond. B, vol. 235, no. 1280, pp. 221-245, Dec. 1988. [16] M.C. Morrone, J. Ross, D.C. Burr, and R. Owens, “Mach bands are phase dependent,” Nature, vol. 324, no. 6049, pp. 250-253, Nov. 1986. [17] M.C. Morrone and R.A. Owens, “Feature detection from local energy,” Pattern Recognit. Letters, vol. 6, no. 5, pp. 303-313, Dec. 1987. [18] P. Kovesi, “Image features from phase congruency,” Videre: J. Comp. Vis. Res., vol. 1, no. 3, pp. 1-26, 1999. [19] L. Zhang, L. Zhang, X. Mou, D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Processing, vol 20, no. 5, pp 2378-2386, August 2011. [20] L. Jin, K. Egiazarian, and C.C. Jay Kuo, “Perceptual Image Quality Assessment Using Block-Based Multi-Metric Fusion (BMMF),” ICASSP 2012, pp. 1145-1148, March 2012 [21] M.M. Cheng, G.X. Zhang, Mitra, N.J., X. Huang and S.M. Hu, “Global contrast based salient region detection,” Computer Vision and Pattern Recognition (CVPR) 2011, pp.409-416, June 2011 [22] P. Felzenszwalb and D. Huttenlocher. “Efficient graph-based image segmentation,” IJCV, 59(2):167–181, 2004. [23] G. Mather, “Foundations of Sensation and Perception,” Oxon, U.K.: Psychology Press, 2008. [24] H. R. Wilson, “Quantitative characterization of two types of line-spread function near the fovea,” Vis. Res., vol. 18, no. 8, pp. 971–981, 1978. [25] H. R. Wilson and J. R. Bergen, “A four mechanism model for threshold spatial vision,” Vis. Res., vol. 19, no. 1, pp. 19–32, 1979. [26] Q. Li, Z. Wang, “Reduced-reference image quality assessment using divisive normalization-based image representation,” IEEE Trans. Image Processing, vol. 3, no. 2, pp. 202-211, 2009 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58171 | - |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:07:26Z (GMT). No. of bitstreams: 1 ntu-103-R01942037-1.pdf: 9431063 bytes, checksum: d9b153d66a7b36218f053e9625817542 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 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 | |
| dc.language.iso | en | |
| dc.subject | 結構相似性 | zh_TW |
| dc.subject | 影像品質評估 | zh_TW |
| dc.subject | 人眼視覺系統 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | difference of Gaussian (DOG) | en |
| dc.subject | support vector regression (SVR) | en |
| dc.subject | FSIM | en |
| dc.subject | SSIM | en |
| dc.subject | PSNR | en |
| dc.subject | color distortion | en |
| dc.subject | Full reference image quality assessment (IQA) | en |
| dc.subject | retina | en |
| dc.subject | human visual system (HVS) | en |
| dc.title | 基於機器學習方法之影像品質鑑定模型 | zh_TW |
| dc.title | Machine Learning Based Image Quality Assessment Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 杭學鳴,鍾國亮,林康平 | |
| dc.subject.keyword | 人眼視覺系統,影像品質評估,結構相似性,支持向量機, | zh_TW |
| dc.subject.keyword | Full reference image quality assessment (IQA),support vector regression (SVR),human visual system (HVS),retina,difference of Gaussian (DOG),color distortion,PSNR,SSIM,FSIM, | en |
| dc.relation.page | 106 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-06-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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