【正文】 . 39。,39。1 1 1 0 0 0 0 0。,... 39。,39。1 0 0 0 0 0 0 0。,... 39。,39。0 0 0 0 0 0 0 0。,... 39。,39。0 0 0 0 0 0 0 0]。,... 39。39。39。,39。I2=blkproc(B2,[8 8],39。P1*x*P239。,T39。,T)。,... 39。])。parent39。units39。points39。tag39。b339。backgroundcolor39。style39。pushbutton39。string39。線條解析39。position39。callback39。cla,39。I=imread(39。39。)。,... 39。,39。imshow(BW)39。b4=uicontrol(39。,h0,... 39。,39。,... 39。,39。,... 39。,[ ],... 39。,39。,... 39。,39。,... 39。,15,... 39。,[80 50 80 30],...39。,39。)。39。X=double(X)/255。imshow(X)。39。致 謝經(jīng)過半年的忙碌和工作，本次畢業(yè)論文設(shè)計已經(jīng)接近尾聲，作為一個本科生的畢業(yè)論文，由于經(jīng)驗的匱乏，難免有許多考慮不周全的地方，如果沒有指導(dǎo)老師的督促指導(dǎo)，以及一起工作的同學(xué)們的支持，想要完成這個設(shè)計是難以想象的。王路露老師多次詢問研究進程，并為我指點迷津，幫助我開拓研究思路，精心點撥、熱忱鼓勵。對王路露老師的感激之情是無法用言語表達的，同時感謝我的家人和朋友在此期間給我的鼓勵和關(guān)懷!此外還要感謝大學(xué)期間的每一位老師和同學(xué)給我的幫助和關(guān)心，在我學(xué)習(xí)上碰到的問題時，我們總是能及時地討論，及時地解決；在我遇到困難時給子我的鼓勵和支持。 學(xué)生簽名：日 期： 2009屆本科生畢業(yè)設(shè)計（論文）資料第二部分 外文資料翻譯Image Compression Using the Discrete Cosine TransformAbstractThe discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency ponents. It is widely used in image pression. Here we develop some simple functions to pute the DCT and to press images. These functions illustrate the power of Mathematica in the prototyping of image processing algorithms.The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and highdefinition television (HDTV) has increased the need for effective and standardized image pression techniques. Among the emerging standards are JPEG, for pression of still images [Wallace 1991]。 and CCITT (also known as Px64), for pression of video telephony and teleconferencing.All three of these standards employ a basic technique known as the discrete cosine transform (DCT). Developed by Ahmed, Natarajan, and Rao [1974], the DCT is a close relative of the discrete Fourier transform (DFT). Its application to image pression was pioneered by Chen and Pratt [1984]. In this article, I will develop some simple functions to pute the DCT and show how it is used for image pression. We have used these functions in our laboratory to explore methods of optimizing image pression for the human viewer, using information about the human visual system [Watson 1993]. The goal of this paper is to illustrate the use of Mathematica in image processing and to provide the reader with the basic tools for further exploration of this subject.The OneDimensional Discrete Cosine TransformThe discrete cosine transform of a list of n real numbers s(x), x = 0, ..., n1, is the list of length n given by:s(u)= C(u) u=0,…nwhere for u=0 =1 otherwiseEach element of the transformed list S(u) is the inner (dot) product of the input list s(x) and basis vector. The constant factors are chosen so that the basis vectors are orthogonal and normalized. The eight basis vectors for n = 8 are shown in Figure 1. The DCT can be writthe product of a vector (the input list) and the n x n orthogonal matrix whose rows are the vectors. This matrix, for n = 8, can be puted as follows:DCTMatrix =Table[ If k==0,Sqrt[1/8],Sqrt[2/8] Cos[Pi (2j+1) k/16] ],{k,0,7},{j,0,7}] // N。 / DCTMatrix, 2] ]]。s DFT function.DCTTwiddleFactors = N Join[{1}, Table[Sqrt[2] Exp[I Pi k /16], {k, 7}]]{1., I, I, I, 1. 1. I, I, I, I}The function to pute the DCT of a list of length n = 8 is then:DCT[list_] := Re[ DCTTwiddleFactors *InverseFourier[N[list[[{1, 3, 5, 7, 8, 6, 4, 2}]]]]]Note that we use the function InverseFourier to implement what is usually in engineering called the forward DFT. Likewise, we use Fourier to implement what is usually called the inverse DFT. The function N is used to convert integers to reals because (in Version ) Fourier and InverseFourier are not evaluated numerically when their arguments are all integers. The special case of a list of zeros needs to be handled separately by overloading the functions, since N of the integer 0 is an integer and not a real. Unprotect[Fourier, InverseFourier]。InverseFourier[x:{0 ..}]:= x。We apply DCT to our test input and pare it to the earlier result puted by matrix multiplication. To pare the results, we subtract them and apply the Chop function to suppress values very close to zero:DCT[input1]{, , , , , , , }% output1 // Chop{0, 0, 0, 0, 0, 0, 0, 0}The inverse DCT can be puted by multiplication with the inverse of the DCT matrix. We illustrate this with our previous example:Inverse[DCTMatrix] . output1{, , , , , , , }% input1 // Chop{0, 0, 0, 0, 0, 0, 0, 0}As you might expect, the IDCT can also be puted via the inverse DFT. The twiddle factors are the plex conjugates of the DCT factors and the reordering is applied at the end rather than the beginning:IDCTTwiddleFactors = Conjugate[DCTTwiddleFactors]{1., + I, + I, + I, 1. + 1. I, + I, + I, + I}IDCT[list_] := Re[Fourier[IDCTTwiddleFactors list] ][[{1, 8, 2, 7, 3, 6, 4, 5}]]For example:IDCT[DCT[input1]] input1 // Chop{0, 0, 0, 0, 0, 0, 0, 0}The TwoDimensional DCTThe onedimensional DCT is useful in processing onedimensional signals such as speech waveforms. For analysis of twodimensional (2D) signals such as images, we need a 2D version of the DCT. For an n x m matrix s, the 2D DCT is puted in a simple way: The 1D DCT is applied to each row of s and then to each column of the result. Thus, the transform of s is given by u=0,…n v=0,…mwhere for u=0 =1 otherwiseSince the 2D DCT can be puted by applying 1D transforms separately to the rows and columns, we say that the 2D DCT is separable in the two in the onedimensional case, each element S(u, v) of the transform is the inner product of the input and a basis function, but in this case, the basis functions are n x m matrices. Each twodimensional basis matrix is the outer product of two of the oned