摘要 移动网络中，客户在观看视频时会出现较多的视频堵塞。这就大大降低了观看视频时的用户体验。在视频服务器端，就会事先保存好不同码率的视频，使得用户在网路阻塞时可以选择低码率视频来保证流畅度。这些不同码率的视频可以由已有视频通过改变分辨率，帧率，量化步长等等不同的方法获得，过程称之为视频转码。网络供应商则会在网络节点中加入视频转码技术，使得节点卡顿时能够通过超分辨率技术令视频图像尽可能恢复到高码率状态，以达到更好的用户体验。25219
    本文主要学习基本的超分辨技术，针对降分辨率转码的情况，研究选用快速的超分辨率技术恢复原有的分辨率提高观赏效果。对基于稀疏表示和自适应字典的超分辨率算法进行了研究。先对稀疏表示做了总体介绍。然后同时训练了高低分辨率字典，将稀疏系数运用在低分辨率字典中，得出系数，再把系数和高分辨率字典联系在一起。从而实现重建的过程。
    本文是在MATLAB2012b的软件环境下进行编译的。过程中实现了对样本的采集、字典的训练、图像的重建等过程。并与其它方法作了比较，全面分析。
毕业论文关键词：视频卡顿，视频转码，码率，超分辨率，稀疏表示，自适应字典
毕业设计说明书外文摘要
Title    Research on super-resolution technology of video resolution transcoding                                               
Abstract
Image super-resolution is defined by a low-resolution image or image sequence to restore the high-resolution images. Image super-resolution technology is pided into super-resolution restoration and super-resolution reconstruction. Currently, the super-resolution image can be pided into three main research category: based interpolation,methods based on reconstruction and learning.
This paper addresses the problem of generating a super resolution (SR) image from a single low resolution input image. We approach this problem from the perspective of compressed sensing: image patches can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation,firstly we trained two image dictionaries of low and high image space characters, seeking a sparse representation for each patch of the low resolution input in the low image dictionary,and then use the coefficients of this representation and high image dictionary to generate a high resolution output.
This is the software environment in MATLAB2012b compiled. The process of realization of the sample collection, dictionary training, image reconstruction and other processes. And compared with other methods, a comprehensive analysis.
Keywords  Video Caton，Video transcoding，code rate，super-resolution,sparse representation,adaptively dictionary learning
目   次
1  引言    1
1.1  研究目的    1
1.2  研究背景    2
1.3  研究现状    3
1.4 文章内容安排    4
2  基于稀疏表示的图像超分辨率算法重建    5
2.2 对单帧图像进行超分辨率重建    8
2.3 图像重建    8
2.4 自行选择字典    10
2.5  原理总结    11
3  实验分析    13
3.1 与其它方法质量对比    13
3.2 选择不同的图像块尺寸    15
3.3 字典大小的影响    16 配合视频降分辨率转码的超分辨率技术研究:http://www.751com.cn/tongxin/lunwen_18885.html