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系統識別號 U0002-2306200515275000
中文論文名稱 基於視覺反應之快速影像縮小與放大
英文論文名稱 Fast Bitmap Images Resizing Based on Human Visual System
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 93
學期 2
出版年 94
研究生中文姓名 黃連進
研究生英文姓名 Lain-Jinn Hwang
學號 886190064
學位類別 博士
語文別 英文
口試日期 2005-05-27
論文頁數 74頁
口試委員 指導教授-趙榮耀
委員-莊淇銘
委員-蔣定安
委員-黃俊堯
委員-方鄒昭聰
中文關鍵字 人類視覺系統  視覺尖銳區域  影像縮放  多項式片段趨近法  像點複製  隨機像點複製  羅森編碼 
英文關鍵字 Human Visual System  Region Of Sharp Area  Image Resize  Piecewise Polynomial Approximation  Pixel Replication  Random Pixel Replication  Rothstein code 
學科別分類 學科別應用科學資訊工程
中文摘要 人類視覺在影像處理扮演重要關鍵。然而,依據幾何光學,我們發現眼睛並不是一個完美的光學系統,它的清晰區域非常小。所以,設計完美的影像產生系統,如果超出眼睛所能感知的能力,將會造成浪費。

基於此一結果,我們提出3個應用領域。第1,「尖銳區域著色法」,用於加速3D場景之產生。第2,「多重解析度影像壓縮法」,可以提高影像壓縮率。第3,「快速影像縮放法」。雖然此法所縮放過的影像產生鋸齒狀,然而其速度比其它方法快達22到40倍,因此相當適合用於虛擬實境,光學字元、符號與車牌辨識。此一演算法沒有用到浮點數運算,所以也相當適合應用於嵌入式系統,如個人數位助理。
英文摘要 Human Visual System plays an important factor in image processing.
However, based on geometric optics, we found the eye is not a
perfect optical system; its “Region of Sharp Area” is very small.
Therefore, it would be wasteful to design a system with perfect image render of which the eye could not utilize.

Based on this result, we introduce three kinds of applications.
First, we investigated a “Region Of Sharp Area” render, with this scheme,
we have successfully speeded up the 3D scenes generation. Secondly, we
proposed a “Multiple Resolution Image Compression” algorithm,
with this method, we have successfully improved the image compression ratio.
Thirdly, we develop a fast image resizing scheme that produces
significantly improved quality over the pixel replication method.
This algorithm is suitable for real-time image resizing applications
including: Virtual Reality, Optical Character Recognition, Symbol Recognition, and Car License Plate Recognition. Although this method produced “jaggies” at the edges in the resized image, the execution time is about 22 to 40 times fast than the Windows and the Weiman scheme, respectively. This scheme doesn't use floating-point operations and buffer area, so, it is suitable for image viewer based on embedded system, such as Portable Digital Assistant.

論文目次 CHAPTER 1 Introductions - 1 -
1.1 Resizing Digital Image - 1 -
1.2 Organization of the Dissertation - 4 -

CHAPTER 2 The Optical Model of Human Visual System - 5 -
2.1 Background - 5 -
2.2 Construction of the Eye - 6 -
2.3 Simplified Model of the Eye - 8 -
2.4 Image Formation in the Eye - 9 -
2.4.1 Image Location and Image Height of thin lens - 9 -
2.4.2 Image Location and Image Size in the Eye - 14 -
2.5 Region Of Sharp Area - 15 -
2.6 Image Confusion of the Eye - 17 -
2.7 Application to Virtual Reality – ROSA render - 21 -
2.8 Application to Image Compression – Multiple Resolutions Image Compression - 26 -
2.9 Summary - 29 -

CHAPTER 3 Methods to Resize Digital Image - 36 -
3.1 Basic Definitions - 36 -
3.2 Digital Image Resizing - 37 -
3.2.1 Interpolation and Spline-based Method - 38 -
3.2.2 Fourier Transform and Cosine Transform(FT/CT) - 41 -
3.2.3 Pixel Replication - 43 -
3.2.4 Area re-sampling - 47 -
3.3 Summary - 47 -

CHAPTER 4 Image Resizing by Random Sampling - 49 -
4.1 Introduction - 49 -
4.2 Proposed Fast Image Resizing Algorithm - 51 -
4.3 Experimental Results - 53 -

CHAPTER 5 Conclusions - 63 -

APPENDIX A Weiman Algorithm in C language - 65 -

Bibliography - 72 -

List of Figures
2.1 The Human Visual System - 6 -
2.2 The Human Eye - 7 -
2.3 Simplified Model of the Eye - 9 -
2.4 Object and Image relationships for a thin lens - 10 -
2.5 Circles of Confusion.(Not to scale.) - 14 -
2.6 Photos with camera focused on middle object, the far and near objects will be blurred - 15 -
2.7 Image Formation in the Eye - 16 -
2.8 (a)Mapping an point from the object plane to the image plane, (b) Circle of Confusion of the eye - 19 -
2.9 Three masks for average filter - 22 -
2.10 Source picture to test the Region of Sharp Area and Confusion of the eye - 23 -
2.11 Partition an image to sharp area and blurry area - 24 -
2.12 Human Visual Effect of Fig 2.10, by using three average filters - 25 -
2.13 Model object as triangles - 26 -
2.14 Area of triangle and Polygon - 26 -
2.15 Generate 3D scenes by small polygon, then enlarge to its original size - 27 -
2.16 Region of Sharp Area render - 31 -
2.17 Polygon render - 32 -
2.18 Block diagram of a Transform Encoder - 32 -
2.19 Zigzag scan - 33 -
2.20 Divided the image into three non-equal area as 1/4, 1/2 and 1/4 in its width and height - 33 -
2.21 An image to test Multiple Resolution Compression scheme - 34 -
2.22 Result generated by the Single Resolution Compression scheme - 34 -
2.23 Result generated by the Multiple Resolution Compression scheme - 35 -

3.1 Matrix representation of a digital image - 37 -
3.2 Enlarge 4 pixels to 5 pixels by Piecewise Polynomial Interpolation - 41 -
3.3 Lena - 43 -
3.4 Zoom Lena by 2 - 44 -
3.5 Rothstein’s code for a line of slope 5/7 - 45 -
4.1 Zoom by Pixel Replication - 50 -
4.2 Sample of Zoom by Pixel Replication - 51 -
4.3 Sample of Zoom by Random Pixel Replication - 53 -
4.4 PSNR and Speedup times of Barbara - 58 -
4.5 Zoom of Baboon - 59 -
4.6 Zoom of Barbara - 60 -
4.7 Zoom of Cman - 61 -
4.8 Zoom of Lena - 62 -

List of Tables
2.1 Some important characteristic of Eye - 8 -
2.2 Render Speed up(Pentium 133MHz, f=2) - 28 -
2.3 Render Speed up(Pentium II 233MHz, f=2) - 28 -
2.4 Improved Multiple Resolutions Image Compression Rate, each picture had 512 × 384 pixels, file size qual to 196608 bytes - 29 -
3.1 Operations require to compute Pl(xi) - 39 -
3.2 Operations require to scale a scan line from w pixels to W pixels - 39 -
3.3 Floating-point Operations require to scale an image from w×h to W×H - 39 -
4.1 PSNR(Enlarge to 320 × 320 then reduce to 256 × 256) - 56 -
4.2 Speedup times(relative to Random pixel replication), enlarge to 320×
320 then reduce to 256 × 256 - 56 -
4.3 PSNR(Reduce to 192 × 192 then enlarge to 256 × 256) - 57 -
4.4 Speedup times(relative to Random pixel replication), reduce to 192 × 192 then enlarge to 256 × 256 - 57 -
參考文獻 Bibliography
[1] W. K. Pratt, Digital Image Processing: PIKS Inside. New York: John Wiley and Sons, Inc., 3rd ed., 2001.
[2] T. M. Lehmann, C. Gonner, and K. Spitzer, “Survey: Interpolation Methods in Medical Image Processing,” IEEE Trans. Med. Imag., vol. 18, pp. 1049–1075, Nov. 1999.
[3] M. Unser, A. Aldroubi, and M. Eden, “Fast B-Spline Transforms for Continuous Image Representation and Interpolation,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 277–285, Mar. 1991.
[4] M. A. Sid-Ahmed, Image Processing: Theory, Algorithms and Architectures. New York: McGraw-Hill, Inc., 1994.
[5] S. A. Martucci, “Image Resizing in the Discrete Cosine Transform Domain,” in IEEE Int. Conf. Image Processing, vol. 2, pp. 244–247, May 1995.
[6] T. C. Chen and R. J. P. D. Figueiredo, “Image Decimation and Interpolation Techniques Based on Frequency Domain Analysis,” IEEE Trans. Communications, vol. 32, pp. 479–484, Apr. 1984.
[7] C. F. R. Weiman, “Continuous Anti-Aliased Rotation and Zoom of Raster Images,” SIGGRAPH ’80 Proc., vol. 14, pp. 286–293, July 1980.
[8] P. Mouroulis, Visual Instrumentation, Optical Design and Engineering Principles. New York: McGraw-Hill, Inc., 1999.
[9] R.Wolfson and J. M. Pasachoff, Physics, with Moderm Physics. Reading, Massachusetts: Addison-Wesley, 1999.
[10] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Englewood Cliffs:Prentice-Hall, 2nd ed., 2002.
[11] W. J. Smith, Modern Optical Engineering. New York: McGraw-Hill, Inc., 3rd ed., 2000.
[12] A. van Meeteren, “Calculations on the optical modulation transfer function of the human eye for white light,” Optica Acta, vol. 21, pp. 395–412, 1974.
[13] B. B. Aaron Lefohn and P. Shirley, “An ocularist’s approach to human iris synthesis,” IEEE Computer Graphics and Application, pp. 70–75, November/December 2003.
[14] S. . L. S. P. Frank L. Pedrotti, Introduction to Optics. Englewood Cliffs: Prentice-Hall, 2nd ed., 1993.
[15] C. M. Cignoni, P and R. Scopigno, “A Comparision of Mesh Simplification Algorithms,” Computer & Graphics, vol. 22, no. 1, pp. 37–54, 1998.
[16] J.-K. Han and S.-U. Baek, “Parametric Cubic Convolution Scaler for Enlargement and Reduction of Image,” IEEE Trans. Consumer Electronics, vol. 46, pp. 247–256, May 2000.
[17] R. L. Burden and J. D. Faires, Numerical Analysis. Boston: Prindle, Weber & Schmidt, 1985.
[18] E. Brigham, The Fast Fourier Transform and Its Applications. Englewood Cliffs:Prentice-Hall, 1988.
[19] J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes, Computer Graphics:Principles and Practice. Reading, Massachusetts: Addison-Wesley, 1990.
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