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研究生:黃俊德
研究生(外文):Jiun-De Huang
論文名稱:以影像分割及邊界描述為基礎的影像壓縮技術
論文名稱(外文):Image Compression by Segmentation and Boundary Description
指導教授:丁建均丁建均引用關係
指導教授(外文):Jian-Jiun Ding
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:2008
畢業學年度:96
語文別:英文
論文頁數:111
中文關鍵詞:邊緣偵測影像切割影像壓縮傅利葉描述子不規則型狀的離散餘弦轉換
外文關鍵詞:Edge detectionImage segmenationImage compressionFourier descriptionArbitrary-shape DCT
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在現有的影像壓縮技術上,如JPEG,皆是對整張圖做相同的處理,而不會對於不同的影像內容而有所調整,所以壓縮率有其極限。而在新一代的影像壓縮技術中,是以影像分割為基礎,將影像盡可能的切割成數個特性或色彩值近似的區塊,每個區塊分別有不同的形狀與色彩值。由於同區塊中的色彩值通常會有高度相關,所以理論上可以產生更高的壓縮率。
對於影像分割,大致上是根據像素值的兩種性質:不連續性與相似性。為了找到不連續的像素值,我們將會介紹影像的邊緣偵測的基本技術並且提出一種結合了傳統的微分法與希爾伯轉換法的可適性方法,叫做短時響應的希爾伯轉換。我們也將會介紹許多其他的方法來做影像切割。我們主要的目的是找出適合的分割結果讓壓縮可以更有效率。
做好影像切割之後,我們會介紹JPEG標準中使用到的壓縮演算法,並應用在我們提出的方法中。為了有效率的記錄每個影像區塊的輪廓,我們先介紹一些常用的邊界描述子並提出兩種改進過後的邊界描述子。為了壓縮影像區塊的色彩值,我們將會討論如何將一個不規則形狀的影像區塊轉換到頻域,接著我們便可以對轉換後的頻率係數做量化及編碼來降低資料量。最後我們將結果與使用JPEG標準壓縮的圖片做比較,證實在可接受的失真範圍下,壓縮率的確可以增加很多。
The present technique of image compression, like the JPEG standard, makes the same process to whole image and does not adjust the parameters based on the local characteristics of the image. Therefore, it has a limit to its compression ratio. However, a new compression technique called segmentation-based image compression has been developed. It segments an image to several regions with similar characteristic or color. Because each image segment has different shapes and color values, we compress these regions individually. Due to the high correlation of the color values in an image segment, we could achieve higher compression ratio in theory.
The technique of image segmentation is based on two properties of color values: discontinuity and similarity. To find the discontinuity of the color values, we will intro-duce the image edge detection technique and propose an adaptive method called the short response Hilbert transform (SRHLT) which combines the traditional differential method and the Hilbert transform method. We will also discuss many other ways to segment an image. The main object is to find a suitable segmented result that can be compressed efficiency.
After Segmentation, we will introduce the basic image compression algorithms in JPEG standard and apply them in our proposed methods. To record the boundary of an image segment efficiently, we will introduce some popular boundary descriptors and propose two improved boundary descriptors. To compress the color values of an image segment, we will discuss how to transform an arbitrary-shape image segment to fre-quency domain. Then we can quantize and encode the frequency coefficients to de-crease the information quantity. Finally, we will compare the result with that of JPEG standard and prove that the compression ratio could be increase a lot under acceptable distortion.
誌謝 i
中文摘要 iii
ABSTRACT v
CONTENTS vii
LIST OF FIGURES xi
LIST OF TABLES xvi
Chapter 1 Introduction 1
Chapter 2 Edge Detection 5
2.1 Differentiation Method for Edge Detection 5
2.1.1 First-Order Derivative Edge Detection 5
2.1.2 Second-Order Derivative Edge Detection 9
2.1.3 The Drawbacks of the Differentiation Method for Edge Detection 12
2.2 Hilbert Transform for Edge Detection 13
2.3 Short Response Hilbert Transform for Edge Detection 16
2.3.1 The Definition of the SRHLT 16
2.3.2 SRHLT of the 2-D Form 20
2.3.3 Experiments of Edge Detection Using SRHLT 22
2.3.4 Illustrated by Canny’s Theorem and Mathematical Analysis 26
2.3.5 Other Possible Ways to Define the SRHLT 29
2.4 Conclusion 33
Chapter 3 Image Segmentation 35
3.1 Thresholding 35
3.2 Edge Linking 37
3.2.1 Local Processing 37
3.2.2 Hough Transform 37
3.3 Region-Based Segmentation 39
3.3.1 Region Growing 40
3.3.2 Segmentation by Morphological Watersheds 41
3.3.3 Mean Shift Based Image Segmentation 43
3.4 Conclusion 45
Chapter 4 Basic Image Compression Algorithm 47
4.1 Color Space Conversion and Downsampling 48
4.2 Transform Coding 50
4.3 Quantization 52
4.4 Entropy Coding Algorithms 54
4.4.1 Huffman Coding 55
4.4.2 Difference Coding 56
4.4.3 Zero Run Length Coding 57
4.5 Simulation Result 60
4.6 Conclusion 61
Chapter 5 Boundary Description and Compression 63
5.1 Polygonal Approximation 63
5.1.1 Merging technique 63
5.1.2 Splitting Technique 64
5.2 Fourier Descriptor 65
Chapter 6 Proposed Methods for Boundary Description and Compression 69
6.1 Second-Order Curve Descriptor 69
6.1.1 Second-Order Polynomial Approximate to a Boundary Segment 69
6.1.2 Splitting Technique with Approximate Second-Order Curve 71
6.2 Fourier Descriptor of Non-Closed Boundary Segments 73
6.2.1 Boundary Segmentation 73
6.2.2 Fourier Descriptor of Non-Closed Boundary Segment 74
6.2.3 Boundary Compression 76
6.2.4 Other Method for Fourier Descriptor of Boundary Segment 78
6.3 Boundary Encoding of the Boundary Segments 79
6.4 Conclusion 82
Chapter 7 Arbitrary-Shape Image Segment Compression 83
7.1 Block-Filled Method 83
7.2 Arbitrary-Shape Image Transform 84
Chapter 8 Proposed Method for Arbitrary-Shape Image Segment Compression 87
8.1 Arbitrary-Shape Transform with DCT Bases 87
8.2 Quantization of the DCT Coefficients 92
8.3 Coding Technique of the Image Segment 93
8.4 Improvement of the Boundary Region by Morphology 97
8.5 Compare with the JPEG Standard 100
8.6 Conclusion 102
Chapter 9 Conclusion and Future Work 103
9.1 Conclusion 103
9.2 Future Work 104
REFERENCE 105
A.Digital Image Processing
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B.Image Compression
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C.Edge Detection
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D.Edge Detection Based on the Hilbert Transform
[38]W. Lohmann, D. Mendlovic, and Z. Zalevsky, "Fractional Hilbert transform," Opt. Lett., vol. 21, pp. 281-283, Feb. 1996.
[39]M. Livadas and A. G. Constantinides, “Image Edge Detection and Segmentation Based on the Hilbert Transform,” ICASSP, vol. 2, pp.1152-1155, 1988.
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[41]J. A. Davis, D. E. McNamara, and D. M. Cottrell, “Image Processing with the Ra-dial Hilbert Transform: Theory and Experiments,” Opt. Lett., vol. 25, no. 2, pp. 99-101, 2000
[42]S. C. Pei and J. J. Ding, “The Generalized Radial Hilbert Transform and Its Ap-plications to 2-D Edge Detection (Any Direction or Specified Directions),” ICASSP, vol. 3, pp. 357-360, Apr. 2003.
[43]J. K. T. Eu and A. W. Lohmann, “Isotropic Hilbert Spatial Filtering“, Opt. Com-mun., vol. 9, no. 3, pp. 257-262, Nov. 1973.

E.Segmentation
[44]R. M. Haralick and L. G. Shapiro, "Image Segmentation Techniques," Computer Vision, Graphics, and Image Processing, vol. 29, pp. 100-132, Jan. 1985.
[45]T. Kanade, "Region Segmentation: Signal vs. Semantics," Computer Vision, Graphics, and lmage Processing, vol. 13, Aug. 1980.
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[48]K. S. Fu and J. K. Mui, "A Survey on Image Segmentation," Pattern Recognition, vol. 13, pp. 3-16, 1981.
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[50]J. S. Weska, "A Survey of Threshold Selection Techniques," Computer Vision, Graphics, and Image Processing, vol. 7, pp. 259-265, 1978.
[51]B. Sankur, A. T. Abak, and U. Baris, "Assessment of Thresholding Algorithms for Document Processing," Proc. IEEE International Conference on Image Process-ing, pp. 580-584, Oct. 1999.
[52]D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 603-619, 2002.
[53]K. Fukunaga, L. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Trans. Information Theory, vol. 21, pp. 32-40, 1975.
[54]R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Toward Objective Evaluation of Image Segmentation Algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, Jun. 2007.

F.Segmentation-Based Image Compression
[55]M. J. Biggar, O. J. Morris, and A. G. Constantinides, "Segmented-image coding: performance comparison with the discrete cosine transform," IEEE Proceedings, 1988.
[56]M. Kunt and M. Kocher, "Second-Generation Image-Coding," Proceedings of the IEEE, pp. 549-574, 1985.
[57]O.-J. Kwon and R. Chellappa, "Segmentation-Based Image Compression," Opti-cal Engineering, vol. 32, pp. 1581-1587, Jul. 1993.
[58]F.-C. Leou and Y.-C. Chen, "A Contour-Based Image Coding Technique with Its Texture Information Reconstructed by Polyline Representation," Signal Process-ing, vol. 25, pp. 81-89, 1991.
[59]L. Shen and R. M. Rangayyan, "A Segmentation-Based Lossless Image Coding Method for High-Resolution Medical Image Compression," IEEE Trans. on Medical Imaging, vol. 16, no 3, pp. 301-307, Jun. 1997.
[60]J. Vaisey, "Image Compression with Variable Block Size Segmentation," IEEE Trans. on Signal Processing, vol. 40, no 8, pp. 2040-2060, Aug. 1992.
[61]M. Bi, S.-H. Ong, and Y.-H. Ang, “A Hybrid Shape-Adaptive Orthogonal Trans-form for Coding of Image Segments,” IEEE trans. circuits syst. video technol, Vol. 10, No. 8, Dec. 2000.

G.Boundary Description and Compression
[62]F. W. Meier, G. M. Schuster and A. K. Katsaggelos, “An Efficient Boundary En-coding Scheme Which Is Optimal In The Rate Distortion Sense,” IEEE, 1997.
[63]G. M. Schuster and A. K. Katsaggelos, “An Optimal Polygonal Boundary Encod-ing Scheme in the Rate Distortion Sense,” IEEE Trans. on Image Processing, vol. 7, no. 1, Jan. 1998.
[64]H. Wang, G. M. Schuster, A. K. Katsaggelos, and T. N. Pappas, “An Optimal Shape Encoding Scheme Using Skeleton Decomposition,” IEEE, 2002.
[65]M. S. Schmalz and G. X. Ritter, “Boundary Representation Techniques for Ob-ject-Based Image Compression,” Proceedings of SPIE, vol. 5208, 2004.
[66]T. A. El Doker and P. A. Mlsna, “Efficient Region Boundary Approximation Us-ing Adaptive Smoothing and Second-Order B-Splines,” IEEE, 2002.
[67]A. J. Pinho, “Encoding of Closed Boundaries Using Transition Points,” IEEE, 1998.
[68]B. J. Mealy, “Region Boundary Generation and Compression,” IEEE, 2001.
[69]R. Tello, “Fourier Descriptors for Computer Graphics,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 25, no. 5, May 1995.
[70]H. Jia, M. Xie, “Improvement of Fourier Descriptor Using Spatial Normaliza-tion,” IEEE Proceedings of ISCIT, 2005.
[71]P.-C. Wang, K.-P. Lin, T.-S. Chen, and P.-T. Hung, “Sectional Contour Interpola-tion Using Fourier Descriptor,” IEEE Proceedings of Medicine and Biology Soci-ety, vol. 20, no. 2, 1998.

H.Arbitrary-Shape Region Compression
[72]W. K. Ng and Z. Lin, “A New Shape-Adaptive DCT for Coding of Arbitrarily Shaped Image Segments,” ICASSP, vol. 4, pp. 2115-2118, 2000.
[73]Shen, B. Zeng, and M. L. Liou, “Arbitrarily Shaped Transform Coding Based on A New Padding Technique,” IEEE trans. circuits syst. video technol, vol. 11, no. 1, Jan. 2001.
[74]B. Mi, C. W. Kuen, and Z. Z. Hang, “Discrete Cosine Transform on Irregular Shape for Image Coding,” IEEE tencon, 1993.
[75]S. F. Chang and D. G. Messerschmitt, “Transform Coding of Arbitrarily-Shaped Image Segments,” Proceedings of the first ACM international conference on Mul-timedia, pp. 83-90, Aug. 1993.

I.Other Technique
[76]J. Kasson and W. Plouffe, "An Analysis of Selected Computer Interchange Color Spaces," ACM Trans. on Graphics, vol. 11, no. 4, pp. 373-405, 1992.
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