毕业设计方案开题报告图像压缩关键技术的研究应用及.doc

河南城建学院本科毕业设计(论文)开题报告 题目： 图像压缩算法研究课 题 类 型： 论文学 生 姓 名： 吕建辉 学 号： 专 业 班 级： 0934101系 别： 电气系指 导 教 师： 石磊开 题 时 间： 3月 年 04 月 10日一、毕业设计内容及研究意义设计内容：本论文重要研究内容是图像压缩技术。详细框架是一方面简介了图像压缩基本原理以及其有关压缩办法分类等理论知识，并且阐明了对图像进行压缩必要性与重要性，然后针对当前图像压缩现状和发展趋势，着重简介了小波变换，并以其为基本来进行数字图像压缩解决，这也许会成为图像数据压缩重要技术之一。接着又依照有关知识编写了某些简朴图像解决程序，对前面理论进行实验、分析、论证。最后，对整篇论文进行总结，发现自身研究局限性，并展望其将来发展前景研究意义：图像信息给人们以直观、生动形象，正成为人们获取外部信息重要途径。然而，数字图像具备极大数据量，在当前计算机系统条件下，要想实时解决，若图像信息不通过压缩，则会占用信道宽，是传播成本变得昂贵，传播速率变慢。这对图像存储、传播及使用都非常不利，同步也阻碍了人们对图像有效获取和使用。此外，随着着计算机科学技术发展，图像压缩技术在通信系统和多媒体系统中重要性也越来越高，在咱们学习、生产、生活以及国防事物中档作用越来越明显。为此，人们予以了图像压缩技术广泛关注，如何用尽量少数据量来表达图像信息，即对图像进行压缩，越来越成为图像研究领域重点课题。二、毕业设计研究现状和发展趋势研究现状：第一代图像压缩编码研究工作是从上个世纪50年代提出电视信号数字后开始，至今己有60近年历史。重要是基于信息论编码办法，压缩比小。1966年J.B.Neal对比分析了差分编码调制(DPCM)和脉冲编码调制(PCM)并提出了用于电视实验数据，1969行了线性预测编码实际实验。同年举办首届图像编码会(PictureCodiSymP0sium)，在这次会议之后，图像压缩编码算法研究有了很大进展。由于DCT压缩算法具备编码效果较好、运算复杂度适中档长处，当前己经成为国际图像编准(JPEG)核心算法。为了克服第一代图像压缩编码存在压缩比小、图像复原质量不抱负等1985年Kunt等人充分运用人眼视觉特性提出了第二代图像压缩编码概念。上世纪80年代中后期，人们相继提出了在多辨别率下表达图像方案，重要方子带压缩编码、金字塔压缩编码等。这些办法均在不同限度上有如下长处:多辨别率信号表达有助于图形信号渐输，不同辨别率信号占用不同频带，便于引入视觉特性。1987年，Mallat次巧妙地将计算机视觉领域内多尺度分析思想引入到小波变换中，统一了在之前各种小波构造办法之后，她又研究了小波变换离散形式，并将相应法应用于图像分解与重构中，为随后小波图像压缩编码奠定了理论基本。1988年Barnsly和S1an共同提出了分形图像编码压缩方案，之后，各国学者提出各种各样改进办法，从而掀起了分形图像编码新高潮。但由于在分形压缩编码过程中，运算量大，从而导致编码时间过长，且提高压缩比同减小失真度之间矛盾始终存在，从而局限了它实用性。上个世纪90年代后，又获得了一系列图像压缩编码研究阶段性新成果，基于零树编码法一方面由A.5.Lewis和G.Knoes提出，其特点是依照小波系数在同方向子带中相似性，即若一种小波系数较小，则很也许高一级(频率更高)同方向子带中相应位置小波系数也较小，运用一种称为小波树树形构造来组织小波系数，使其能以便地去除频域和空间域中有关性。接着Shapir结合比特平面编码办法设计了更好零树编码办法，Shapri提出嵌入式零树小波算法是迄今为止最有效办法，它有效地运用了小波系数特性，实现了图像可分级编码，但是不同限度地存在算法时间复杂度和空间复杂度过高弱点。当前，小波变换图像压缩编码算法已成为图像压缩研究领域一种重要方向，基于小波变换图像编码技术正逐渐显示出它优越性，其中小波变换己被JPEG-国际原则采用。发展趋势：随着数字化技术迅速发展，数字图像也被越来越多运用在咱们寻常学习、生活、工作当中，图像压缩技术将会非常重要。从国际数据压缩技术发展特别是MPEG发展可以看出，基于内容图像压缩编码办法是将来编码发展趋势。它不但能满足进一步获得更大图像数据压缩比规定，并且可以实现人机对话功能。此外，任意形状物体模型建立核心问题还没有解决，这严重影响其应用广泛性。因而，图像编码将朝着多模式和跨模式方向发展。通过元数据进行编码也是此后编码发展方向。元数据是指详细描述音/视频信息基本元素，运用元数据来描述音视频对象同步也就完毕了编码，由于此时编码对象是图像一种描述而不再是图像自身。从另一种角度来说，进一步提高压缩比，提高码流附属功能（码流内容可访问性、抗误码能力、可伸缩性等）也将是将来编码两个发展方向。三、毕业设计研究方案及工作筹划1、研究方案本次论文撰写拟从图像数据压缩必要性出发，阐述本课题研究义，然后简朴简介了图像压缩编码技术发呈现状，最后对本文重要工作以及各章内容编排做了阐明。接下来对数字图像压缩必要性、重要性以及现今国内外对于这一技术领域研究现状简介开始，进而系统并简要述说图像压缩理论知识。紧接着将对为什么要选用小波变换为基本，来进行数字图像压缩解决进行详细且详细阐述，并且简介了小波变换对静止图像进行编码技术，阐述了Matlab算法，最后附上有关程序，通过实验来验证本论文所选办法优越性。最后是对本次论文总结。详细有如下几方面工作：（1）、简介图像及图像压缩有关知识，弄清晰图像定义、基本概念及压缩编码基本过程，并且重点讨论几种老式、在图像压缩中被广泛采用编码办法及其基本原理。（2）、通过度析比较小波变换和老式变换办法，发现小波变换在图像压缩技术领域优越性。同步，从小波基选取、原始信号边界延拓、小波系数量化、熵编码、小波分解/重构级数来逐渐分析小波编码基本思想、理论。（3）、量化和编码间关系是小波变换压缩核心所在。本论文中将用EZW与SPIHT算法来解决这种关系，即研究该算法重要思想、实现环节，并阐述、编写基于Matlab软件有关程序，通过程序运营来验证所用办法优越性。2、重点和难点本次论文重难点是，对为什么要选用小波变换为基本，来进行数字图像压缩解决进行：（1）要简介小波变换背景及基本理论、函数，其中涉及对于老式变换办法局限性（对瞬态和局部信号分量分析、时频和空频局部化）阐述，小波变换分类及其他；（2）分析小波参数选取及其对图像压缩先后质量影响，以及如何运用小波系数分布特点和小波图像特点，对系数进行组织和编码，实现数字图像压缩；（3）简介两种典型基于小波变换图像压缩编码算法即EZW算法和SPIHT算法，并研究其重要思想、详细实现环节，同步，认真分析各种算法长处和缺陷试图寻找可以改进地方。最后阐述了Matlab算法，运用小波变换理论进行有关程序编写，通过实验来验证所选办法优越性。3、工作筹划学生姓名吕建辉专业电子信息工程起止日期（日/月）周次内 容 进 程备 注3.01-3.071接受设计课题，查找有关参照文献和资料。3.08-3.142熟悉设计课题，查阅、整顿参照文献和资料3.15-3.213学习有关参照文献和资料。3.22-3.284理清思路，撰写开题报告3.29-4.045开题答辩，对设计课题内容作初步论证4.05-4.116内容论证，内容改进，内容定稿4.12-4.187对小波变换进行研究学习4.19-4.258对小波变换进行研究学习4.26-5.029对各种图像压缩办法研究学习5.03-5.0910比较各种图像压缩办法优缺陷5.10-5.1611熟悉毕业论文格式、撰写论文草稿5.17-5.2312撰写论文草稿5.24-5.3013完毕论文草稿并提交5.31-6.0614自我修改毕业论文6.07-6.1315自我修改毕业论文6.14-6.2016依照教师指引，改进局限性之处，总体完善6.21-6.2717完毕论文终稿，提交论文终稿6.28-7.0418准备好自述讲稿，打印，参加论文答辩四、重要参照文献:1 阮秋琦. 数字图像解决学M. 北京：电子工业出版社，2 赵荣椿. 数字图像解决导论M. 西安：西北工业大学出版社，19993 K.R.Castleman. 数字图像解决M. 北京：电子工业出版社，4 钟诚.小波变换及其应用研究J.中华人民共和国科技信息,25 彭玉华.小波变换与工程应用M.北京:科学出版社,19996 余松煌，张文军.孙军等著.当代图像信息压缩技术M .科学出版社，19987 姜丹.信息论与编码M.合肥中华人民共和国科学技术大学出版社，8 黄贤武.数字图像解决与压缩编码技术M .成都:电子科技大学出版社，2009 刘榴梯，刘明奇.党长民等著.实用数字图像解决M .北京理工大学出社，1998.10 马平.数字图像解决和压缩M.电子工业出版社,11 尹显东，李在铭，姚军等著.图像压缩原则研究发展与前景M.中华人民共和国工程物研究院信息与电子工程，.12.12 张太怡，吴晓芸，张双腾等著.基于JPEG国际原则图像压缩办法研究J .重庆大学学报，1994，17(5)13 AnalogDeviees.ADV-JPJPEGCo-ProeessorJ.PreliminaryTeehniealData，.14 TAYLOR W F. The Geometry of Computer GraphicsM. Wadsworth Inc，199215 A.J.Patti，M.I.Sezan A.M.Tekalp. Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time. IEEE Trans，199716 Elad M Feuer A. Restoration of a Single Superresolution Image from Several Blurred Noisy and Undersampled Measured Images. IEEE Trans，199717 J.K.Paik，A.K.Katsaggelos. Iamge restoration using a modified Hopfield network. J IEEE Transactions on Image Processing，1992 18 闫阳,张正炳.基于小波变换图像压缩编码J.当代电子技术,，28(3)19 王晓辉,朱光喜,朱耀庭.图像一种分形特性表达法及其应用J.电子学报,1997,25(l0)20 练华,宋宝瑞.基于小波变换分形图像编码J.上海交通大学学报,(4)21 张兢,路彦和.具备感兴趣区域静止图像压缩编码算法研究J.计算机应用,(6)22 JOEL AM,Larsson C,Charilaos C.Region of Interest Coding in JPEG J.Signal rocessing:Imag Communication,(17)附： 一篇引用外文文献Analysis of Compression Encoding about Digital ImageYufang Gao Yang LiuBeijing University of Posts and Telecommunications,Beijing,PRC,100876Abstract :This paper mainly investigate the theory of image pressing encoding,two parts,the technology and process of encoding,are included.In the technology of encoding,we give a deep study in thetraditional methods and the use of those methods in the image compression,for example estimation coding,DCT transform,data quantization,entropy coding.Estimation coding lowers the time relativity of image data.DCT transform lowers the space relativity of image data.Data quantization makes use of the redundancy of mentality and vision.Entropy coding brings down the redundancy of coding.After those process,image data will be effectively compressed.Based on the coding technology,this paper examplified MPEG-2 standard discusses the process of the serial of motion image,which includes frame and field coding mode.This paper particularly expounds the field coding of motion compensation combined with DCT transform,introduces the theory of motion compensation and the count of motion vector.Motion compensation makes better use of relativity of field data and increases the compensation ratio.Key words： estimation coding DCT transform motion compensation motion estimation MPEG-21. IntroductionDigital images have many advantages，but the digital image of the massive amount of data hinders the development of digital image technology. In recent years，image compression coding have made rapid development,the sign is International research on image compression coding drawn up a series of compression standards，such as：JPEG，H.26X series，MPEG series。At the same time the development of VLSI technology makes high-performance image coding special chips as possible，which led to the golden age of digital image communication development.Digital color television image signal commonly used component encoding ,luminance signal sampling frequency is 13.5MHZ，color difference signals of the sampling frequency 6.75MHZ. After component encoded，The rate of which composed of three component signals TDM stream is：（13.5+6.75+6.75）8=216Mb/s，so the space for digital video Per minute is：216Mb/s60s/8=1620MB。Such a huge amount of data makes a 650MB CD can only store one minute of video images，even a 10GB hard drive also can not store a few minutes of video images，it is necessary to compress image data.In this paper,we investigate the method that using MATLAB Wavelet Transform for compression. Methods：The image was processed by wavelet transform，and then the information of low frenquency was retained and the information of high frenquency was home zero. Results：The quality of the first image compression was high，but the compression ratio was low. The compression ratio of the second time was higher,and the image quality was good. Conclusion：The method is simple，reliable and effective in image compression.With the computer technology and the rapid development of network technology，images，sound and other multimedia information recording，storage，transmission has been digitized，the huge amount of data brought to the storage and transmission of certain difficulties，digital image compression has become a solution to the problem key technology. Over the last decade ,wavelet theory has become a new direction in applied mathematics. As a mathematical tool，wavelet was quickly applied to the image and voice of many other fields. Wavelet transform is a signal of time - scale analysis method，the characteristics of multi-resolution analysis，and in time and frequency domain has the capacity of local signal characteristics，a fixed window size，its shape variable，time window and frequency window can change the time-frequency localization analysis，that low frequency high frequency resolution and time resolution，the high frequency part of the high time resolution and lower frequency resolution very suitable for detection of transient entrainment of the normal signal anomalies and show its components，so the microscope known as signal analysis.2，Image Compression Coding TheoryCoding process can be summarized as： Original image Mapping transformation Quantizer Entropy Coder data streamMapping transformation reduces the correlation between image data，Make it more conducive to compression；Quantizer to map the data into a binary digital signal；Entropy code make that appeared on the probability of a large source of symbols assigned to the short code；Small symbol on the probability assigned to a long code，there by reducing redundant data generated code. Data compression is divided into lossless and lossy compression. Lossless compression means that image data after compression can be completely restored，restored the same image and original image，while lossless compression refers to the compressed image data while maintaining the characteristics of the original image under the premise of inevitable loss of part of the original image is not important information. The current image based on wavelet transform compress gradually replaced the DCT and the other based on the coding technique，a new image compression international Biaozhun of choice，such as the most advanced image compression standard JPG the core algorithm is wavelet transform.2.1 Wavelet Analysis Principles and MethodsWavelet analysis is currently applied mathematics and engineering disciplines in a fast-growing new field. Wavelet is a small wave，small means it has the Decay；wave refers to its volatility. With the increasing maturity of wavelet theory，wavelet analysis has become very broad applications. Image processing is an important area of application of wavelet analysis，image processing has become a useful tool.2.2The principle of wavelet transform image compression Wavelet transform for image compression the basic idea is：multi-resolution decomposition of the image，broken down into different space，different frequency sub-image，and then pairs the image coefficients are encoded. Wavelet transform coefficient coding is the core for image compression，compression is the real coefficient quantization compression. Image after wavelet transform wavelet image data generated and the total amount of data equal to the original image，that wavelet transform itself does not have compression. The reason why it used for image compression，because the resulting wavelet image and the original image with different characteristics，and in the image of the energy concentrated in the low frequency part，while the horizontal，vertical and diagonal part of the energy is less；level,vertical anddiagonal part of the characterization of the original image in the horizontal，vertical and diagonal part of the edge information，with clear direction characteristics. Called low-frequency part of the image brightness，horizontal，vertical and diagonal part of the known details of the image Therefore，the simplest method is to use wavelet compression to preserve low-frequency part of the high frequency part of the set 0. Original image is first decomposed into low frequency and high frequency horizontal component L HL1，high-frequency vertical component LH1，high-frequency diagonal components HH1，and then further decompose the low frequency component L repeatedly to the desired level of decomposition. As the scaling function with low-pass filter (H) the role of the wavelet function has a high pass filter (G)，the role of image wavelet decomposition for the equivalent of the horizontal and vertical filtering and sub-sampling，the reverse process of the image shall reconstruction 3. 2.3 Image Compression using Mat lab programming stepsCompressed image using wavelet transform under the following three steps: The discrete wavelet transform image is decomposed into low frequency and high frequency components similar to the level of high-frequency vertical and diagonal details of the high frequency component； Extract low frequency，high-frequency part of the set 0； Using the inverse wavelet transform of the reconstructed image .3 ResultsIn this study,we use MATLAB 6.5 programming. Using wavelet decomposition to remove the high frequency part of the image while only retaining the low frequency part of the image as a simple compression method. Function that is used with wavedec2 bior3.7 wavelet decomposition of the image layer，then in low frequency coefficients appcoef2 function，and finally quantified with wcodemat function code. The results are as follows: Original image The first compressed image The second compressed imageImage size before compressionName Size Bytes ClassX 256 256 524288 double arrayGrand total is 65536 elements using 524288 bytesThe first compressed image sizeName Size Bytes Classca1 128 128 131072 double arrayGrand total is 16384 elements using 131072 bytesAfter the second compressed image sizeName Size Bytes Classca2 64 64 32768 double arrayGrand total is 4096 elements using 32768 bytesFrom the above experimental results can be seen that the first compression ratio is 27.81% of before compression. The second compression ratio is 8.58% of before compression. Can be seen from the figure that the effect of the two compression is good，especially the first time the compression is better. The second compressed image darker ,its reason is the loss caused by a large number of low frequency coefficients. It also verified the second compressed greater.4 DiscussionThese results indicate that the image wavelet transform，remove the high frequency part，to retain low frequency，can be better compressed image data，to a certain compr