【摘要】：近年來,計(jì)算機(jī)技術(shù)發(fā)展迅速,處理能力日新月異,這也給互聯(lián)網(wǎng)、視頻及電視廣播領(lǐng)域帶來了改革之風(fēng)。作為信息的重要載體,視頻技術(shù)也隨之不斷發(fā)展進(jìn)步。視頻編碼一直是視頻處理領(lǐng)域的熱點(diǎn)研究課題,視頻的高分辨率、立體化發(fā)展導(dǎo)致視頻數(shù)據(jù)日益增加,高效的壓縮編碼技術(shù)顯得至關(guān)重要。壓縮感知理論作為一個(gè)全新的采樣理論,撼動(dòng)了信息處理領(lǐng)域的基石——奈奎斯特采樣定理。壓縮感知理論可以在不丟失重構(gòu)原信號(hào)所需信息的情況下,采用遠(yuǎn)低于傳統(tǒng)方法的采樣頻率,用最少的觀測(cè)次數(shù)來采樣信號(hào),實(shí)現(xiàn)信號(hào)的降維處理。分布式壓縮感知理論結(jié)合了壓縮感知理論與分布式信源編碼理論,可以充分利用信號(hào)內(nèi)及信號(hào)間的相關(guān)性,受到無線傳感網(wǎng)絡(luò)(WSN)、雷達(dá)及視頻編碼等領(lǐng)域廣泛關(guān)注。本文主要針對(duì)分布式壓縮感知視頻編解碼進(jìn)行研究,所做的工作如下:首先,本文對(duì)壓縮感知重構(gòu)算法及分布式壓縮感知理論進(jìn)行了研究,然后針對(duì)第一聯(lián)合稀疏模型JSM1提出了一種聯(lián)合重構(gòu)算法,該算法充分利用信號(hào)之間的相關(guān)性,在減少測(cè)量值數(shù)目的情況下,加速了信號(hào)的重構(gòu),并保證了信號(hào)重構(gòu)的精確度。然后,本文基于視頻特性,結(jié)合聯(lián)合稀疏理論和分布式視頻技術(shù),對(duì)基于分布式壓縮感知的視頻編碼技術(shù)進(jìn)行了研究。針對(duì)視頻編碼數(shù)據(jù)量大,采集耗時(shí)等特點(diǎn),引入分布式壓縮感知,提出了一種結(jié)構(gòu)簡(jiǎn)單,處理高效的視頻編解碼框架。在本框架中,采用聯(lián)合稀疏模型減少計(jì)算量,在提供快速編解碼的同時(shí),實(shí)現(xiàn)了圖像的精確重構(gòu)。最后,本文對(duì)圖像稀疏模型進(jìn)行了研究,結(jié)合視頻聯(lián)合稀疏的特點(diǎn),將樹稀疏模型引入分布式壓縮感知中,提出了森林稀疏模型,該模型充分挖掘視頻圖像幀的樹稀疏結(jié)構(gòu)及聯(lián)合樹稀疏結(jié)構(gòu)特點(diǎn),能夠較快的解碼視頻圖像。實(shí)驗(yàn)結(jié)果表明,該模型下的視頻重構(gòu)更為簡(jiǎn)單穩(wěn)定。綜上所述,本文提出的兩種分布式壓縮感知視頻編碼方案的性能要明顯優(yōu)于獨(dú)立使用壓縮感知進(jìn)行編解碼的視頻編碼方案的性能。
[Abstract]:In recent years, the rapid development of computer technology, processing power with each passing day, which has brought the Internet, video and television broadcasting reform. As an important carrier of information, video technology has been developing and improving. Video coding has always been a hot research topic in the field of video processing. The high resolution and three-dimensional development of video leads to the increasing number of video data. Efficient compression coding technology is very important. As a new sampling theory, compressed sensing theory has shaken the cornerstone of information processing field-Nyquist sampling theorem. The compressed sensing theory can use the sampling frequency far lower than the traditional method without losing the information needed to reconstruct the original signal and sample the signal with the least number of observations to realize the signal dimensionality reduction processing. The theory of distributed compression sensing combines the theory of compression sensing with the theory of distributed source coding, which can make full use of the correlation between signals and signals. It has attracted wide attention in the field of (WSN), radar and video coding in wireless sensor networks. This paper mainly focuses on the research of distributed compressed perceptual video coding and decoding. The work is as follows: firstly, this paper studies the algorithm of compressed perception reconstruction and the theory of distributed compressed sensing. Then a joint reconstruction algorithm is proposed for the first joint sparse model (JSM1). The algorithm makes full use of the correlation between signals, accelerates the reconstruction of signals and ensures the accuracy of signal reconstruction under the condition of reducing the number of measured values. Then, based on the characteristics of video, combined with sparse theory and distributed video technology, this paper studies the video coding technology based on distributed compression perception. In view of the characteristics of large amount of video coding data and time-consuming collection, a simple and efficient video coding and decoding framework is proposed by introducing distributed compression perception. In this framework, the joint sparse model is used to reduce the computation cost, and the accurate reconstruction of the image is realized at the same time of fast coding and decoding. Finally, the sparse image model is studied in this paper. Combined with the characteristics of video sparsity, the tree sparse model is introduced into the distributed compression awareness, and the forest sparse model is proposed. The model fully exploits the tree sparse structure of video frame and the sparse structure of joint tree, which can decode the video image quickly. The experimental results show that the video reconstruction based on this model is simpler and more stable. To sum up, the performance of the two distributed compressed perceptual video coding schemes proposed in this paper is obviously superior to that of the video coding schemes using compression awareness independently.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN919.81

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本文編號(hào)：2410239