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姓名	方志筠(Chih-Yun Fang)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	基於線段扭曲與幾何變形之影像濃縮畫質衡量機制 (Quality Assessment of Image Retargeting based on Line Bending and Geometric Distortion)
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摘要(中)	影像濃縮(retargeting)的目標是讓具有固定尺寸的影像資料在各種解析度的畫面輸出中都能有良好的成像。然而，不同的影像濃縮方法往往對於調整後影像有相異的效果，因此接近使用者主觀感受的畫質衡量機制是必要的。本研究針對在具有原始影像做為參考下的濃縮影像提出客觀的畫質衡量機制，本機制分成兩個部分，包括重要線段扭曲(Line Bending)與幾何形狀變形(Geometric Distortion)，兩者都會利用SIFT Flow 去判別像素點在兩張圖中的的位移情形。我們主要檢視SIFT Flow 的變化情況決定影像的幾何變形程度，另一方面根據視覺顯著圖(Saliency Map)在畫面上標示重要線段，再以SIFT Flow 找出線段扭曲位置，當幾何變形被判定失效時改以線段扭曲程度評量畫質。評量的參數設定儘量貼近平均意見分數(MOS)，期望與人眼的主觀感受一致。實驗結果測試了不同影像與濃縮方法的評估結果，並與其他的客觀衡量方式比較，以顯示我們所提出的機制符合主觀感受的準確程度。
摘要(英)	Image retargeting is a technique to output images with a different aspect ratio from those of displaying devices. Various methods exist but they may not be consistent with different kinds of images. It is essential to develop good quality assessment approaches in this field. In this research, we propose an objective quality assessment for image retargeting with original images as the reference. The proposed scheme includes two parts: line bending and geometric distortion, both of which are based on SIFT Flow to examine the degree of pixel shifting. The proposed scheme basically employs the variation of SIFT Flow to determine the geometric distortion. At the same time, some important lines are drawn according to saliency map to determine line distortion, which is used to grade the quality when the geometric distortion is not reliable. The setting of the parameters aims at making the score closer to the mean opinion scores (MOS), which represents that the quality assessment is consonant with human’s vision. Experimental results show the accuracy of the proposed scheme by comparing with other objective image quality assessment methods and different retargeting approaches.
關鍵字(中)	★ 影像畫質衡量 ★ 畫面扭曲 ★ 重要線段 ★ SIFT Flow	關鍵字(英)	★ Image quality assessment ★ Distortion ★ Important line ★ SIFT Flow
論文目次	論文摘要 ................................... i Abstract .................................. ii 致謝 ...................................... iii 第一章 緒論 ................................. 1 1.1 研究背景與動機 .......................... 1 1.2 研究重要貢獻 ............................ 3 1.3 論文架構 ............................... 4 第二章 相關研究 ............................. 5 2.1 主觀衡量 ................................ 8 2.2 客觀衡量 ............................... 10 第三章 實作方法 ............................. 13 3.1 系統概述 ................................ 13 3.2 SIFT Flow .............................. 14 3.3 Line Bending ........................... 16 3.3.1 Saliency Map ......................... 17 3.3.2 Important Line ....................... 19 3.4 Geometric Distortion.................... 20 3.5 Score .................................. 22 第四章 實驗結果 ............................. 25 4.1 與平均意見分數(MOS)比較 ........................................... 25 4.2 與其他客觀演算法比較 ........................................... 29 4.3 套用至不同濃縮影像(Retargeted image) .... 32 第五章 結論與未來方向 ....................... 35 5.1 結論 .................................. 35 5.2 未來方向 .............................. 35 參考文獻 .................................. 37
參考文獻	[1]D. Vaquero, M. Turka, K. Pullib, M. Ticob, and N. Gelfandb, ”A survey of image retargeting techniques,” Proceedings of SPIE the International Society for Optical Engineering, vol. 7798, p. 779814, 2010. [2]RetargetMe Benchmark [Online]. Available: http://people.csail. mit.edu/mrub/retargetme/index.html [3]Ma, L., Lin, W., Deng, C. and Ngan, K. (2012). “Image Retargeting Quality Assessment: A Study of Subjective Scores and Objective Metrics.” IEEE Journal of Selected Topics in Signal Processing, 6(6), pp.626-639. [4]Y. Zhang, W. Lin, X. Zhang, Y. Fang, and L. Li, “Aspect ratio similarity (ARS) for image retargeting quality assessment,” in Proc.of IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2016, pp. 1080-1084. [5]Hsu, C., Lin, C., Fang, Y. and Lin, W. (2014). “Objective Quality Assessment for Image Retargeting Based on Perceptual Geometric Distortion and Information Loss.” IEEE Journal of Selected Topics in Signal Processing, 8(3), pp.377-389. [6]Ce Liu, Yuen, J. and Torralba, A. (2011). “SIFT Flow: Dense Correspondence across Scenes and Its Applications.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), pp.978-994. [7]F. Stentiford, ”Attention based auto image cropping,” The 5th International Conference on Computer Vision Systems, Bielefeld, 2007. [8]I. S. Amrutha, S. S. Shylaja, S. Natarajan, and K. N. Murthy, ”A smart automatic thumbnail cropping based on attention driven regions of interest extraction,” Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, ACM, pp. 957-962, 2009. [9]P. Cheatle, ”Automatic image cropping for republishing,” IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, pp. 75400O-75400O-9, 2010. [10]L. Itti, C. Koch, and E. Niebur, ”A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, 1998. [11]S. Avidan, and A. Shamir, ”Seam carving for content-aware image resizing,” ACM Transactions on graphics (TOG), vol. 26, no. 3, Aug 2007. [12]M. Rubinstein, A. Shamir, and S. Avidan, ”Improved seam carving for video retargeting,” ACM Transactions on Graphics (TOG), vol. 27, no. 3, p. 16, 2008. [13]M. Grundmann, V. Kwatra, M. Han, and I. Essa, ”Discontinuous seam-carving for video retargeting,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, June 2010. [14]D. Domingues, A. Alahi, and P. Vandergheynst, ”Stream carving: an adaptive seam carving algorithm,” 17th IEEE International Conference on Image Processing (ICIP), pp. 901-904, 2010. [15]R. Gal, O. Sorkine, and D. Cohen-Or, ”Feature-aware texturing,” Proceedings of the 17th Eurographics conference on Rendering Techniques, Eurographics Association, June 2006. [16]L. Wolf, M. Guttmann, and D. Cohen-Or, ”Non-homogeneous content-driven video-retargeting,” IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, 2007. [17]Y. S. Wang, C. L. Tai, O. Sorkine, and T. Y. Lee, ”Optimized scale-and-stretch for image resizing,” ACM Transactions on Graphics (TOG), vol. 27, no. 5, 2008. [18]S. Hua, G. Chen, H. Wei , and Q. Jiang, ”Similarity measure for image resizing using SIFT feature,” EURASIP Journal on Image and Video Processing, pp. 1-11, Jan 2012. [19]M. Rubinstein, A. Shamir, and S. Avidan, ”Multi-operator media retargeting,” ACM Transactions on Graphics (TOG), vol. 28, no. 3, 2009. [20]W. Dong, N. Zhou, J. C. Paul, and X. Zhang, ”Optimized image resizing using seam carving and scaling,” ACM Transactions on Graphics (TOG), vol. 28, no. 5, p.125, 2009. [21]Wang, Y., Tai, C., Sorkine, O. and Lee, T. (2008). “Optimized scale-and-stretch for image resizing.” ACM Transactions on Graphics, 27(5), p.1. [22]Fang, Y., Chen, Z., Lin, W. and Lin, C. (2012). Saliency Detection in the Compressed Domain for Adaptive Image Retargeting. IEEE Transactions on Image Processing, 21(9), pp.3888-3901. [23]Rubinstein, M., Shamir, A. and Avidan, S. (2009). “Multi-operator media retargeting.” ACM Transactions on Graphics, 28(3), p.1. [24]S. Montabone, and A. Soto., ”Human detection using a mobile platform and novel features derived from a visual saliency mechanism,” Image and Vision Computing, vol. 28, no. 3, pp. 391-402, 2010. [25]David G. Lowe, “Object recognition from local scale-invariant features,” ICCV ′99 Proceedings of the International Conference on Computer Vision-Volume 2, Page 1150, 1999 [26]NTHU Retargeting Image Dataset (NRID) [Online]. Available: http://www.ee.nthu.edu.tw/cwlin/Retargeting_Quality/NRID.html [27]Image Retargeting Subjective Database [Online]. Available: http://ivp.ee.cuhk.edu.hk/projects/demo/retargeting/index.html [28]Y.C., Chou, P. C., Su, “Toward More Efficient Multi-Operator Media Retargeting for Digital Images and Videos”, National Central University, 2016.
指導教授	蘇柏齊(Po-Chyi Su)	審核日期	2017-7-27
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