English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54371/62179 (87%)
Visitors : 8733957      Online Users : 138
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTHU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/86538


    Title: 使用群試的小波圖像壓縮
    Authors: 杜昇展
    Du, Sheng-Jhan
    Description: GH02101021504
    碩士
    數學系
    Date: 2014
    Keywords: 小波壓縮;圖像壓縮;群試理論;逐步演算法
    wavelets;image compression;group testing;sequential algorithm
    Abstract: 電腦多媒體影像、網路的蓬勃發展,使得影像的應用愈來愈頻繁。要如何讓影像可以壓縮的愈小,使其可以在有限的儲存空間存入更多的影像,使其可以在網路上更快地傳送,就成為了一個相當值得探討與研究的焦點。
    小波影像壓縮是現在流行的一種影像壓縮技術,它透過將圖形轉換成小波係數並利用係數間的性質來達到壓縮的目的。其中一個關鍵的步驟,是如何將一個稀疏的01矩陣編碼成一串較短的01序列,從群試的角度出發這可以視為一個從矩陣中找出少數1的位置的問題,而群試正是用來有效率地找出藏在相對大量的目標物裡的少數特定物的一種方法。於是Hong, E.S. 與 Ladner, R.E發展了一套基於群式的編碼方法-GTW。本篇將會介紹並定義GTW的數學架構,並從群試理論的角度去探討GTW的另一種演算法。
    The development of Internet and multimedia makes the usage of image are important issue. How to store more images in limited memory space and how to transmit images quickly on Internet becomes important topics. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. Therefore, in order to achieve these goals, image compression plays an important role.

    Wavelet image compression is a popular image compression technology. It turns an image into wavelet coefficients and use the correlation between wavelet coefficients to achieve the purpose of compression. One of the key issue is how to encode a “sparse” binary matrix, as short as possible, into a series of binary bits which contains the information of the original matrix. This can be regarded as a problem of identifying the 1's from the matrix.

    Group testing is a subject that deals with identifying a small number of targets from a large number of objects efficiently. The basic idea is the group test. Given a subset $S$ of $M$, a group test on $S$ has two outcomes: “positive” means there exists at least one target in $S$; “negative” means there is no target in $S$. For a group test on a subset $S$ which is large, we can instantly exclude a large amount of objects from targets if the outcome is negative. On the other hand, it will not take many tests to identify a target if the outcome is positive and the subset is small. Through clever choices of subsets we can identify the few targets from a large number of objects efficiently. Since there are only 2 outcomes in each group test, we can use 1 to represent positive to a group test and use 0 to represent negative respectively. Thus, a series of group tests can output a series of binary bits and this can be regarded as a coding method.
    Hong, E.S. and Ladner, R.E developed a coding method based on group testing called GTW which gives a framework of group testing for wavelet image compression.

    In this thesis, we first introduce and define the mathematical framework of GTW. And then explore the improvement of GTW from the perspective of group testing.
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/86538
    Source: http://thesis.nthu.edu.tw/cgi-bin/gs/hugsweb.cgi?o=dnthucdr&i=sGH02101021504.id
    Appears in Collections:[數學系] 博碩士論文

    Files in This Item:

    File SizeFormat
    GH02101021504.pdf78KbAdobe PDF121View/Open


    在NTHUR中所有的資料項目都受到原著作權保護,僅提供學術研究及教育使用,敬請尊重著作權人之權益。若須利用於商業或營利,請先取得著作權人授權。
    若發現本網站收錄之內容有侵害著作權人權益之情事,請權利人通知本網站管理者(smluo@lib.nthu.edu.tw),管理者將立即採取移除該內容等補救措施。

    SFX Query

    與系統管理員聯絡

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback