請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/37528
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 貝蘇章 | |
dc.contributor.author | Meng-Ting Lin | en |
dc.contributor.author | 林孟婷 | zh_TW |
dc.date.accessioned | 2021-06-13T15:31:26Z | - |
dc.date.available | 2009-07-16 | |
dc.date.copyright | 2008-07-16 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-15 | |
dc.identifier.citation | Chapter 2
[1] J.L. Bentley, J.H. Friedman, Data structure for range searching, Computing Surveys, vol.11, no.4, pp.397-409, Dec. 1979. [2] P. Heckbert, Color image quantization for frame buffer display, ACM Trans. Computer Graphics (SIGGRAPH) 16 (3), pp.297-307, 1982. [3] A. Kruger, Median-cut color quantization, Dr. Dobb’s Journal, pp.46-92, September 1994. [4] G. Joy, Z. Xiang, Center-cut for color-image quantization, The Visual Comput. Vol.10 pp.62-66, 1993. Chapter 3 [5] Y. Sirisathitkul, S. Auwatanamongkol, B. Uyyanonvara, Color image quantization using distances between adjacent colors along the color axis with highest color variance, Pattern Recognition Letters, Vol.25, pp.1025-1043, 2004. [6] M. Gervautz, W. Purgathofer, A simple method for color quantization: Qctree quantization, In: Glassner, A. (Ed.), Graphics Gems. Academic Press, New York, pp.287-293, 1990. [7] K.F. Hwang, C.C. Chang, A fast pixel mapping algorithm using principal component analysis, Pattern Recognition Lett. 23 (14), pp.1747-1753, 2002. [8] S.C. Pei, Y.S. Lo, Color image compression and limited display using self-organizing kohonen map, IEEE Trans. Circuits Systems Video Technol. 8 (2), pp.191-205, 1998. Chapter 4 [9] W. J. Lin, J, C. Lin, Color Quantization by preserving color distribution features, Signal Processing, Vol.78, pp. 201-214, 1999. [10] S.C. Cheng, C.K. Yang, A fast and novel technique for color quantization using reduction of color space dimensionality, Pattern Recognition Lett. 22 (8), pp.845-856, 2001. [11] C.K. Yang, W.H. Tsai, Color image compression using quantization, thresholding, and edge detection techniques all based on the moment preserving principle, Pattern Recognition Lett. 19 (2), pp.205-215, 1998. [12] R. Balasubramanian, J.P. Allebach, A new approach to palette selection for color images, Proc. SPIE: Human Vision Visual Process. Digital Display Ⅲ, Vol.1453, pp.58-69, 1991. [13] K.M. Kim, C.S. Lee, E.J. Lee, Y.H. Ha, Color image quantization and dithering method based on human visual system characteristics, J. imaging Sci. Technol, Vol.40 (6), pp.502-509, 1996. [14] R.S. Gentile, J.P. Allebach, E. Walowit, Quantization of color images based on uniform color spaces, J. Imaging Technol, Vol.16 (1) , pp.12-21, 1990. [15] K.E. Spaulding, L.A. Ray, J.R. Sullivan, Secondary quantization of color images for minimum visual distortion, Proc. SPIE: Human Vision Visual Proc. Digital Display IV, vol.1913, pp.261-269, 1993. [16] R. C. Gonzolez, R. E. Woods, Digital image processing second edition, Prentice Hall, 2002 Chapter 5 [17] T.H. Kim, J. Ahn, M.G. Choi, Image dequantization: restoration of quantized colors, Computer Graphics Forum, Vol.26, No.3, 619-626, Sep2007. [18] Y.H. Fung, Y.H. Chan, An iterative algorithm for restoring color-quantized images, In Proc. International Conference on Image Processing, pp.313-316, 2002. [19] W.T. Freeman, T.R. Jones, E.C. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications 22, 2, pp.56-65, 2002. [20] N.P. Galatsanos, R.T. Chin, Restoration of color images by multichannel Kalman filtering, IEEE Transactions on Signal Processing 39, 10, pp.2237-2252, 1991. [21] A. Levin, D. Lischinski, Y. Weiss, Colorization using optimization, ACM Transactions on Graphics 23,3, pp.689-694, 2004. [22] M. Ben-Ezrz, S.K. Nayar, Motion-based motion deblurring, IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 6 ,pp.689-698, 2004. [23] D.V.D. Ville, M. Nachtegael, D.V. Derweken, E.E. Kerre, W. Philips, I. Lemahieu, Noise reduction by fuzzy image filtering, IEEE Transactions on Fuzzy Systems 11, 4, pp.439-436, 2003. [24] T. Weissman, E. Ordentlich, G. Seroussi, Universal discrete denoising: Known channel, IEEE Transactions on Information theory 21, 1, pp.5-28, 2005. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/37528 | - |
dc.description.abstract | 彩色影像量化是期望在人眼沒有感受到差異的情況下,將彩色影像由較多的色彩數量簡化為較少的表示色彩數量。彩色影像量化對於輸出能顯示的色彩數量不足時,是一個很有用的方法。此外它也可以節省儲存空間和傳輸時間。因此,彩色影像量化在現在是一個很重要的議題。
在這篇論文中,我們將會介紹四種影像量化的演算法。前兩種是廣為人知的方法:中位數切割法和中心點切割法。這兩種方法的切割平面是由平衡數值總類的數量來決定。第三種方法藉由計算在像素數值鄰近點的距離的量化法,主要希望可以使量化誤差達到最小。第四種方法是藉由保持量化前後色彩分佈不變的量化法。藉由保持量化前後色彩分佈不變的特性,我們還可以做其他的應用。 最後會介紹一種不需要知道影像量化方法的重建方法。期望在視覺上可以將量化過後的影像重建回沒量化過,主要是可以消除量化時產生的影像輪廓。 | zh_TW |
dc.description.abstract | The color image quantization expects that a color image can use less number of representative colors to represent it and the human eyes can not notice. It is helpful when the output do not have enough colors to display. Also, it can save storage memory and the transmit time. Thus the color image quantization is an important issue in nowadays.
In this thesis, we will introduce four kinds of image quantization algorithms. The pervious two are well-know: the median-cut and the center-cut algorithm. The cutting plane is decided by balancing populations. The third one is called quantization using distances between adjacent colors algorithm which wishes to minimal the total quantization errors. The Fourth one is quantization by preserving color distribution features, which want to keep the color distribution unchanged. By preserving the same color distributions, we also do some applications. Last, we will illustrate a color image dequantization algorithm which is blind. It will improve the color contours and make the quantized color image looks like an original one. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T15:31:26Z (GMT). No. of bitstreams: 1 ntu-97-R95942094-1.pdf: 8598111 bytes, checksum: 5e82ef37d3aefa0965a1c430f6d55903 (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 iii 中文摘要 v ABSTRACT vii CONTENTS ix LIST OF FIGURES xi LIST OF TABLES xv Chapter 1 Introduction 1 Chapter 2 Previous Works of Color Image Quantization 3 2.1 Introduction 3 2.2 Median-Cut Color Quantization Algorithm 3 2.3 Center-Cut Color Quantization Algorithm 11 2.4 Conclusion 17 Chapter 3 Color Image Quantization Using Distances between Adjacent Colors 19 3.1 Introduction 19 3.2 Proposed Color Quantization Algorithm 19 3.3 Experimental Results 25 3.4 Conclusion 30 Chapter 4 Color Image Quantization by Preserving Color Distribution Features 31 4.1 Introduction 31 4.2 Proposed Color Quantization Algorithm 31 4.3 Experimental Results 37 4.4 Applications by Preserving Color Distribution Features 50 4.4.1 Introduction 50 4.4.2 Edge Detection 50 4.4.3 Interpolation 54 4.4.4 Image Fusion 59 4.4.5 Experimental Results 63 4.5 Conclusion 66 Chapter 5 Image Dequantization 67 5.1 Introduction 67 5.2 Inverse Quantization 67 5.3 Experimental Results 70 5.4 Conclusion 75 Chapter 6 Conclusion and Future Works 77 6.1 Conclusion 77 6.2 Future Works 78 REFERENCE 79 | |
dc.language.iso | en | |
dc.title | 數位彩色影像之量化與重建 | zh_TW |
dc.title | Quantization and Dequantization of the
Digital Color Image | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 鐘國亮,鄭伯順,曾建誠 | |
dc.subject.keyword | 影像量化,影像重建,影像處理, | zh_TW |
dc.subject.keyword | image quantization,dequantization,image process, | en |
dc.relation.page | 83 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2008-07-15 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-97-1.pdf 目前未授權公開取用 | 8.4 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。