本文選題：壓縮感知 + 字典學(xué)習(xí)　； 參考：《天津大學(xué)》2014年碩士論文

【摘要】：壓縮感知理論是近年來提出的一種信號壓縮編碼理論,它突破了奈奎斯特采樣定理的極限,能以隨機(jī)采樣的方式用更少的采樣數(shù)據(jù)優(yōu)質(zhì)的恢復(fù)出原始信號。信號的稀疏表示是壓縮感知理論的基礎(chǔ)和前提,因此如何找到合適的稀疏字典,實(shí)現(xiàn)信號的最優(yōu)稀疏表示,成為該領(lǐng)域的重要研究目標(biāo)。在眾多稀疏字典中,基于字典學(xué)習(xí)的自適應(yīng)過完備稀疏字典擺脫了固定結(jié)構(gòu),使得字典中的原子尺度和特性更加接近原始信號本身。然而已有字典學(xué)習(xí)算法存在耗時(shí)過長的問題,因此提高字典學(xué)習(xí)速度的研究有著重要的意義。近年來,無線多媒體傳感器網(wǎng)絡(luò)中的視頻編解碼方法得到了越來越多的重視。該領(lǐng)域的研究主要針對兩大問題:(1)如何降低編碼端的復(fù)雜度;(2)如何抵抗信道誤碼。壓縮感知理論在該領(lǐng)域的應(yīng)用,可以很好的解決上述問題,應(yīng)用字典學(xué)習(xí)算法則可提高視頻重構(gòu)精度,因此將壓縮感知和字典學(xué)習(xí)應(yīng)用于視頻編解碼中,有很大的應(yīng)用前景。本文主要包括以下三個(gè)研究內(nèi)容:(1)分析各類稀疏字典的特點(diǎn),并將其應(yīng)用于壓縮感知理論中,通過實(shí)驗(yàn)對字典結(jié)構(gòu)、稀疏表示能力和重構(gòu)精度等方面做了詳細(xì)對比;(2)基于字典學(xué)習(xí)耗時(shí)過長的問題,提出了一種改進(jìn)的字典學(xué)習(xí)算法IK-SVD,在稀疏表示環(huán)節(jié)引入了系數(shù)復(fù)用思想,在字典更新環(huán)節(jié)對SVD分解方法進(jìn)行簡化,從而減小時(shí)間損耗。實(shí)驗(yàn)數(shù)據(jù)表明,該算法將字典學(xué)習(xí)時(shí)間縮短了1/3以上;(3)針對傳統(tǒng)編碼方式在無線多媒體傳感器網(wǎng)絡(luò)中的局限性,提出了一種基于字典學(xué)習(xí)的壓縮感知視頻編解碼模型,該模型采用壓縮感知理論將計(jì)算復(fù)雜度從編碼端轉(zhuǎn)移到了解碼端,字典學(xué)習(xí)算法的加入實(shí)現(xiàn)了視頻重構(gòu)精度的提高。理論分析和仿真實(shí)驗(yàn)表明該模型是可行并且有效的。
[Abstract]:Compression sensing theory is a signal compression coding theory proposed in recent years. It breaks through the limit of Nyquist sampling theorem and can recover the original signal with less sampling data by random sampling.Sparse representation of signals is the basis and premise of compressed sensing theory. Therefore, how to find appropriate sparse dictionaries and realize optimal sparse representation of signals has become an important research goal in this field.Among many sparse dictionaries the adaptive overcomplete sparse dictionaries based on dictionary learning get rid of the fixed structure and make the atomic scale and characteristics of the dictionary closer to the original signal itself.However, existing dictionary learning algorithms are time-consuming, so it is very important to improve the speed of dictionary learning.In recent years, more and more attention has been paid to video coding and decoding in wireless multimedia sensor networks.The research in this field focuses on two major problems: 1) how to reduce the complexity of the encoder and how to resist the channel error.The application of compressed perception theory in this field can solve the above problems well. The application of dictionary learning algorithm can improve the accuracy of video reconfiguration. Therefore, the application of compression perception and dictionary learning in video coding and decoding has great application prospects.This paper mainly includes the following three contents: 1) analyzing the characteristics of all kinds of sparse dictionaries, and applying them to the theory of compression perception.In this paper, the sparse representation ability and reconstruction accuracy are compared in detail. Based on the problem of long time consuming in dictionary learning, an improved dictionary learning algorithm, IK-SVD, is proposed, which introduces the idea of coefficient reuse in sparse representation.The SVD decomposition method is simplified in dictionary update to reduce time loss.Experimental data show that the algorithm shortens dictionary learning time by more than one third. Aiming at the limitation of traditional coding methods in wireless multimedia sensor networks, a compressed perceptual video codec model based on dictionary learning is proposed.In this model, the computational complexity is transferred from the coding end to the decoding end using the compression sensing theory, and the precision of video reconstruction is improved with the addition of dictionary learning algorithm.Theoretical analysis and simulation results show that the model is feasible and effective.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN919.81;TN911.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉芳;武嬌;楊淑媛;焦李成;;結(jié)構(gòu)化壓縮感知研究進(jìn)展[J];自動(dòng)化學(xué)報(bào);2013年12期

2 王蓉芳;焦李成;劉芳;楊淑媛;;利用紋理信息的圖像分塊自適應(yīng)壓縮感知[J];電子學(xué)報(bào);2013年08期

3 武曉嘉;郭繼昌;姜丹;薛陳;;一種多視點(diǎn)立體視頻分布式壓縮感知編解碼模型[J];天津大學(xué)學(xué)報(bào);2012年10期

4 趙瑞珍;秦周;胡紹海;;一種基于特征值分解的測量矩陣優(yōu)化方法[J];信號處理;2012年05期

5 方紅;楊海蓉;;貪婪算法與壓縮感知理論[J];自動(dòng)化學(xué)報(bào);2011年12期

6 甘偉;許錄平;蘇哲;張華;;基于貝葉斯假設(shè)檢驗(yàn)的壓縮感知重構(gòu)[J];電子與信息學(xué)報(bào);2011年11期

7 焦李成;楊淑媛;劉芳;侯彪;;壓縮感知回顧與展望[J];電子學(xué)報(bào);2011年07期

8 張宗念;黃仁泰;閆敬文;;壓縮感知信號盲稀疏度重構(gòu)算法[J];電子學(xué)報(bào);2011年01期

9 石光明;劉丹華;高大化;劉哲;林杰;王良君;;壓縮感知理論及其研究進(jìn)展[J];電子學(xué)報(bào);2009年05期

10 張春梅;尹忠科;肖明霞;;基于冗余字典的信號超完備表示與稀疏分解[J];科學(xué)通報(bào);2006年06期

相關(guān)博士學(xué)位論文 前1條

1 鄧承志;圖像稀疏表示理論及其應(yīng)用研究[D];華中科技大學(xué);2008年

，

本文編號：1745191