预测编码与变换编码在图像压缩中的应用与分析

信息计算科学论文编号:XXLW011 论文字数:10130,页数:30  附任务书,开题报告,文献综述，外文翻译

目录
封面........................................................................i
中文摘要....................................................................ii
英文摘要....................................................................iii
概述.................................................................1
 1.1 图像压缩的意义.....................................................1
 1.2 图像压缩技术分类...................................................2
 1.3 预测编码与变换编码概述.............................................3
第二章 预测编码..............................................................5
      2.1 预测编码原理.......................................................5
      2.2 线性预测...........................................................6
      2.3非线性自适应预测.......................... .........................8
第三章 变换编码..............................................................12
      3.1 正交变换...........................................................12
          3.1.1连续函数集合的正交性.........................................12
          3.1.2 二维离散正交变换.............................................12
          3.1.3 正交变换的物理意义...........................................14
      3.2 傅立叶变换.........................................................15
          3.2.1 一维傅立叶变换...............................................15
          3.2.2 二维傅立叶变换...............................................17
      3.3 K-L变换............................................................18
          3.3.1 K-L变换原理................................................. 18
      3.4 离散余弦变换.......................................................19
          3.4.1 离散余弦变换原理.............................................19
          3.4.2 快速DCT算法.................................................20
第四章 离散余弦变换的Matlab实现.................... ........................23
      4.1 Matlab 简介........................................................23
      4.2 离散余弦变换的Matlab实现..........................................23
结束语.......................................................................28
致谢.........................................................................29
参考文献.....................................................................30

摘  要
 
 在信息世界迅猛发展的今天,人们对计算机实时处理图像信息的要求越来越高。如何在保证图像质量的前提下,同时兼顾实时性和高效性成了一个值得关注的问题。于是,对图像信息进行一定的压缩处理成为了一个不可或缺的环节。图像压缩是关于用最少的数据量来表示尽可能多的原图像的信息的一个过程。
    本文主要研究图像压缩编码理论，包括两个部分内容：编码技术和编码流程。在编码技术中，我们详细介绍了预测编码、DCT变换编码、数据量化、熵编码的原理及其在图像压缩中的应用。预测编码减弱了图像数据的时间相关性；DCT变换编码减弱了数据的空间相关性；数据量化利用了人眼心理视觉冗余；熵编码减小了编码冗余。减小了这些冗余，图像数据将得到有效的压缩。
　　在编码技术基础之上，系统地讨论了运动图像视频序列的编码流程，包括帧内编码模式和帧间编码模式。其中重点研究了运动补偿帧间预测结合DCT变换的帧间压缩编码，分析了运动补偿的原理和运动矢量的计算方法。

关键词 正交变换;图像压缩;快速DCT算法;Matlab

A Deep Analysis of Similarities And Predictive Coding And Transformation Coding

Abstract

 Today,information industry is developing swift and violent,people of real-time computer image information processing increasingly high demands. How to guarantee the quality of the image,taking into account real-time performance and efficiency has become a matter of concern. Thus,processing compression and disposal of the image information will be an indispensable link. Image compression is a process of the least amount of data to show as many as possible of the original image information.
　　traditional 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, 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 :orthogonal transformation;image compression;Fast Algorithm for DCT;Matlab