臺灣博碩士論文加值系統

近年來隨著個人電腦技術的快速演進,電腦週邊設備也被發展出來及廣泛應用。噴墨印表機方面是電腦週邊中最普及者之一,它除了被用來列印文字資料外,最常見的應用就是被用來列印影像。因為噴墨印表機是一種二元的輸出裝置,所以當我們要列印一張8位元連續色調的影像時,經過半色調化處理後,透過噴墨印表機輸出就會損失7/8的資訊量。既然透過噴墨印表機來輸出影像必然會有影像資訊的損失,那我們在列印的前提之下,可以事先考慮影像因為半色調化所造成的損失,以便在量化的時候將會在列印步驟的損失去除,來有效地減少影像資料的大小。
在本論文中, 我們考慮發展以數位半色調為基礎之JPEG影像壓縮技術。透過結合半色調演算法與人類視覺系統,我們得以設計出完全符合JPEG標準影像壓縮程序的量化表。透過這個程序所設計出的JPEG量化表能夠有效地提高影像的壓縮率,而且能夠列印出品質優良的影像。我們所提出的量化表設計程序並不預設半色調演算方式,因此可適用於各種半色調演算法。

Recently due to the fast progress of the personal computer technology, various computer peripheral equipments were developed and are widely used. The inkjet printer is one of the most popular computer peripheral. It has been used for printing text data and image as well, the most application is the image printing. Because the inkjet printer is a binary output device, when we want to print an 8-bit continuous-tone image approximately seven-eighth information in the original images will be lost during the halftoning process. Since we will eventually lose some information during the halftoning process when we print an image using inkjet printers, we can consider incorporating the information loss due to halftoning in the JPEG quantization stage. By doing so, we can reduce the required harddisk space for storing the image data.
In this thesis, we develop JPEG image compression techniques based on digital halftone printing. By incorporating the concepts of halftoning algorithm and the human visual system in the JPEG compression process, we can design a new quantization table that completely conforms to the JPEG image compression standard. We will show that the compression rate can be increase and excellent quality image prints can be obtained by using new quantization table in the JPEG image compression process. The quantization table design procedure proposed in this thesis does not presume a particular halftone algorithm, so all kinds of halftoning algorithm can be used to design the quantization table.

第一章 緒論 1
1.1 研究背景 ................................1
1.2 研究動機與目的 ..........................2
1.4 章節內容概要 ............................3
第二章 半色調演算法 4
2.1 半色調與人類視覺系統 ....................4
2.1.1 半色調簡介 ............................5
2.1.2 人類視覺系統 ..........................7
2.2 抖色演算法 ..............................9
2.3 誤差擴散法 .............................15
2.4 半色調影像優劣的分析與盼判斷 ...........17
第三章 影像壓縮原理與JPEG標準 19
3.1 轉換編碼之影像壓縮系統 .................19
3.2 JPEG影像壓縮標準 .......................23
3.2.1 色彩模式轉換與取樣頻率 ...............23
3.2.2 離散餘弦轉換 .........................26
3.2.3 量化 .................................27
3.2.4 編碼 .................................28
第四章 結合半色調演算法的量化表設計 33
4.1 量化表的設計............................34
4.1.1 半色調誤差的計算 .....................24
4.1.2 Kam等人所提之量化誤差的計算 ......... 36
4.1.3 我們所提之量化誤差的計算 .............38
4.1.4 量化表設計之演算法 ...................41
4.2 影響量化表的因素 .......................43
第五章 電腦模擬結果與分析 48
5.1 群聚抖色演算法的結果分析 ...............50
5.2 散置抖色演算法的結果分析 ...............51
5.3 誤差擴散演算法的結果分析 ...............55
5.4 綜合比較 ...............................58
第六章 結論 63

ITU-T Rec. T.81-ISO/IEC no. 10918-1, Information
Technology-Digital Compression and Coding of Continuous-Tone Still Images,Part I:Requirements and Guidelines, 1993.
Robert Ulichney, Digital Halftoning, The MIT Press Cambridge, MA, USA, 1987.
郭正智,"彩色影像最佳半色調化演算法之研究,"國立海洋大學電機工程研究所碩士論文,2001
R. A. Vander Kam, P. W. Wong and R. M. Gray, ``JPEG-Complinat Perceptual Coding for a Grayscale Image Printing Pipeline," IEEE Trans. Image Processing, vol. 8, no. 1, Jan. 1999.
T. N. Pappas and D. L. Neuhoff,"Least-Squares model-based
halftoning,"in Proc. 1992 SPIE/IS&T Symp.Electronic Imaging Science Technology. vol. 1666,San Jose,CA,Feb. 1992,pp.165-176.
Pingshan Li and Jan P. Allebach,"Look-Up-Table based halftoning
algorithm,"IEEE Trans. Image Processing ,vol. 9. no. 9,pp 1593-1603,September 2000.
R. A. Ulicheny,"Dithering with Blue Noise," Proceedings of IEEE,vol. 76, no. 1,pp56-79,January 1989.
T. Mista,"Evaluation of halftone techniques using psychovisual
testing and quantitative quality measure,"SPIE. vol. 1666,Human Vision, Visual Processing,and Digital Display III,pp. 177-187,1992.
J. O. Limb,"Distortion criteria of the human viewer,"IEEE. Trans
on Systems,Man,and Cybernetics,SMC-9(12),778-793,Dec. 1979.
E. Yeung, ``Image compression using wavelets, "Proceedings of
the IEEE Conference on Electrical and Computer Engineering, vol. 1, pp. 241-244, May, 1997.
G. K. Wallace, ``The JPEG still picture compression standard,"
IEEE Trans. Commu. of the ACM, vol. 34, no. 4, pp.30-44,Apr. 1991.
施威銘研究室, PC影像處理技術(二)-圖檔壓縮篇, 旗標出版社, 台北, 1994.
R. C. Gonzalez and R. E. Woods, Digital Image Processing,
Addison Wesley, 1992.
S. J. Leon, Linear Algebra with Application, Prentice Hall,
New Jersey, USA, 1998.
R. A. Vander Kam, P. W. Wong and R. M. Gray, ``JPEG compression for a grayscale printing pipeline," Proc. SPIE, 1995, vol. 2418, pp. 229-240.
J. L. Mannos and D. J. Sakrison, ``The effects of a visual fidelity criterion on the encoding images, "IEEE Trans. Inform. Theory, vol. IT-20, pp. 525-536, July 1974.
D. S. Taubman and M. W. Marcellin,JPEG2000 : image compression fundamentals, standards, and practice, Kluwer Academic Publishers, Boston, 2002.