【摘要】：壓縮感知理論提供了將模擬信號(hào)直接采樣壓縮為數(shù)字形式的有效途徑,具有直接信息采樣的特性。在該理論框架下,信號(hào)的采樣和壓縮同時(shí)以遠(yuǎn)低于奈奎斯特采樣率的極低速率進(jìn)行,顯著地降低了數(shù)據(jù)采集、存儲(chǔ)和傳輸代價(jià),以及信號(hào)處理時(shí)間和計(jì)算成本,具有重要的軍用和民用價(jià)值�；趬嚎s感知的視頻采集與重構(gòu),所需測(cè)量值的數(shù)目遠(yuǎn)小于傳統(tǒng)采樣方法獲得的數(shù)據(jù)量,因而可以大幅降低信號(hào)的采樣和存儲(chǔ)成本,從而降低對(duì)采集器件的要求和實(shí)現(xiàn)難度;同時(shí)可以在視頻信號(hào)采集的同時(shí)實(shí)現(xiàn)壓縮,大大降低了編碼器復(fù)雜度,減少了對(duì)內(nèi)存和運(yùn)算資源的需求,使得在資源受限環(huán)境中可以實(shí)現(xiàn)低成本的(超)大分辨率視頻采集和壓縮。但是,直接將壓縮感知理論應(yīng)用于視頻信號(hào),往往會(huì)因?yàn)閭鹘y(tǒng)的正交變換系數(shù)的稀疏性很難達(dá)到壓縮感知重構(gòu)的要求而導(dǎo)致較差的重構(gòu)質(zhì)量;并且由于傳統(tǒng)的壓縮感知重構(gòu)算法僅考慮了信號(hào)的稀疏性特征,并未考慮到信號(hào)自身的其他結(jié)構(gòu)特征,而使其很難達(dá)到最優(yōu)的視頻重構(gòu)質(zhì)量。由于視頻信號(hào)區(qū)別于一般信號(hào)最大的特點(diǎn)是存在大量空間/時(shí)間冗余,因此如何利用相關(guān)性是研究視頻壓縮感知理論的主要難題,目前在國內(nèi)外仍處于研究的起步階段。本文以壓縮感知理論為基礎(chǔ),在保證視頻采樣終端低復(fù)雜度的前提下,以提高視頻壓縮感知重構(gòu)質(zhì)量為目的,圍繞著視頻信號(hào)的高效稀疏重構(gòu)展開研究,旨在傳統(tǒng)的壓縮感知框架下通過引入視頻信號(hào)空時(shí)稀疏的結(jié)構(gòu)特征,將傳統(tǒng)的非自適應(yīng)結(jié)構(gòu)變?yōu)樽赃m應(yīng)的視頻壓縮感知框架,以減少編碼測(cè)量所需的采樣率并提高視頻重構(gòu)質(zhì)量。本文以國家自然科學(xué)基金、高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金等項(xiàng)目為研究平臺(tái),對(duì)視頻壓縮感知重構(gòu)的關(guān)鍵技術(shù)進(jìn)行研究。全文內(nèi)容主要針對(duì)基于支撐集的壓縮感知理論、視頻壓縮感知編碼端速率控制、解碼端高效重構(gòu)算法以及分布式框架下視頻壓縮感知重構(gòu)等四個(gè)方面展開研究,具體概括為:1)針對(duì)一般信號(hào),研究有支撐集輔助的壓縮感知理論及其在視頻壓縮感知中的應(yīng)用。由于傳統(tǒng)壓縮感知理論的約束等距性很難在實(shí)際應(yīng)用中被驗(yàn)證,故本文主要在相關(guān)性判別理論框架下研究有支撐集的壓縮感知重構(gòu)問題。本文在理論上證明了如果預(yù)測(cè)支撐集能夠在精度與尺寸上滿足一定條件,那么利用加權(quán)1l范數(shù)優(yōu)化即可得到穩(wěn)定的稀疏解;并且相比于沒有支撐集的相關(guān)性判別條件,本文證明了利用支撐集可以得到更弱的充分條件以及更優(yōu)的重構(gòu)誤差限。2)在視頻壓縮感知框架下研究速率控制算法,以實(shí)現(xiàn)自適應(yīng)的視頻采樣,從而能夠在不增加采樣率的前提下進(jìn)一步提高視頻整體的重構(gòu)質(zhì)量。具體來說,由于采樣終端無法得到視頻信號(hào)像素域的結(jié)構(gòu)特征,因而使得在視頻壓縮感知框架下研究速率控制備受挑戰(zhàn)。本文首先提出了一種新穎的視頻壓縮感知失真模型;然后利用該模型設(shè)計(jì)了采樣率與量化比特深度的聯(lián)合優(yōu)化算法,通過求解率失真優(yōu)化問題實(shí)現(xiàn)率失真意義下最優(yōu)的采樣率與比特深度估計(jì),進(jìn)而能夠在實(shí)現(xiàn)目標(biāo)碼率的同時(shí)得到最優(yōu)的視頻壓縮感知重構(gòu)質(zhì)量。仿真實(shí)驗(yàn)結(jié)果表明,利用本文提出的速率控制算法可以較好地控制視頻壓縮測(cè)量的碼率,并且相比于傳統(tǒng)視頻壓縮感知系統(tǒng)可大幅提高重構(gòu)的率失真性能。3)利用視頻信號(hào)空時(shí)稀疏的結(jié)構(gòu)特征,研究高效的視頻壓縮感知重構(gòu)算法。具體來說,本文提出了一種正則化的加權(quán)基追蹤去噪重構(gòu)方法,通過預(yù)測(cè)視頻信號(hào)的支撐集和像素值來輔助當(dāng)前幀重構(gòu),并且基于交替方向乘子法構(gòu)造了一種快速的迭代算法以實(shí)現(xiàn)該問題的求解。此外,本文通過分別構(gòu)造視頻信號(hào)在像素域與測(cè)量域的幀間相關(guān)模型,提出了一種基于最優(yōu)相關(guān)模型的視頻壓縮感知重構(gòu)方法,并構(gòu)造了一種基于二階Bregman分裂的迭代算法來實(shí)現(xiàn)該優(yōu)化問題的求解。仿真結(jié)果表明,本文算法能夠通過充分利用視頻信號(hào)的結(jié)構(gòu)特征實(shí)現(xiàn)高效重構(gòu),并且相比與傳統(tǒng)方法能夠提供更好的采樣率-失真性能與主觀圖像質(zhì)量。4)本文最后重點(diǎn)針對(duì)分布式視頻壓縮感知,研究討論視頻重構(gòu)相關(guān)的問題。具體來說,本文首先在分布式視頻壓縮感知框架下研究當(dāng)前幀與邊信息幀的相關(guān)性,并構(gòu)造了一種新穎的欠采樣相關(guān)噪聲模型。然后,在此基礎(chǔ)上,本文提出了一種基于最大似然字典訓(xùn)練的分布式視頻壓縮感知系統(tǒng),和一種基于字典學(xué)習(xí)和1l分析重構(gòu)二者聯(lián)合優(yōu)化的系統(tǒng),以及該框架下基于交替方向乘子法的迭代重構(gòu)算法。仿真實(shí)驗(yàn)結(jié)果表明,本文算法相比于傳統(tǒng)分布式視頻壓縮感知方法,均能夠提供更好的重構(gòu)質(zhì)量。
[Abstract]:The compressed sensing theory provides an effective way to compress the direct sampling of analog signals into digital forms, with the characteristics of direct information sampling. In this theoretical framework, the sampling and compression of signals are far lower than the very low rate of Nyquist sampling rate, which significantly reduces the cost of data acquisition, storage and transmission, and the signal. Processing time and computational cost are of great military and civil value. Video acquisition and reconstruction based on compressed sensing are far less than the amount of data obtained from traditional sampling methods. Thus, the sampling and storage costs of the signals can be reduced greatly, thus the requirements and difficulties of the acquisition devices can be reduced; at the same time, it can be reduced. The simultaneous compression of video signal acquisition reduces the complexity of the encoder, reduces the demand for memory and computing resources, and makes low cost (super) high resolution video acquisition and compression in the resource constrained environment. However, the application of compressed sensing theory to video signals is often due to the traditional orthogonal design. The sparsity of the transform coefficients is difficult to achieve the requirements of the compressed sensing reconstruction and lead to the poor reconstruction quality. And because the traditional compression sensing reconstruction algorithm only takes into account the sparsity of the signal, it does not take into account the other structural features of the signal itself, and makes it difficult to achieve the best quality of the video reconfiguration. The largest characteristic of the general signal is the existence of a lot of space / time redundancy, so how to use the correlation is the main problem to study the video compression perception theory. At present, it is still at the beginning of the research at home and abroad. Based on the compression perception theory, this paper improves the video pressure on the premise of guaranteeing the low complexity of the video sampling terminal. In order to reduce the quality of perceptual reconstruction, this paper focuses on the efficient and sparse reconstruction of video signals. In the traditional compressed sensing framework, the traditional non adaptive structure is transformed into an adaptive video compression perception framework by introducing the spatial time sparsity of video signal, so as to reduce the sampling rate and improve the view of the coding measurement. In this paper, the key technology of video compression perception reconstruction is studied on the National Natural Science Foundation and the special scientific research fund of the doctoral discipline point of the University. The full text is mainly based on the compression perception theory based on the support set, the rate control of video compression perceptual coding end, and the efficient reconstruction of the decoder The algorithm and the video compression perception reconstruction under the distributed framework are studied in four aspects, which are summarized as follows: 1) the compression perception theory with support set assisted and its application in video compression perception are studied for the general signal. The constraint ISO distance of the traditional compression theory is difficult to be verified in practical applications. It is necessary to study the problem of compressed sensing reconstruction with support set under the framework of correlation discriminant theory. This paper theoretically proves that if the predictive support set can satisfy a certain condition on the precision and size, then the stable sparse solution can be obtained by using the weighted 1L norm optimization, and compared to the correlation criterion of the unsupported set, this method can be obtained. It is proved that using the support set can obtain more weak sufficient conditions and better reconstruction error limit.2), the rate control algorithm is studied under the video compression perception framework to realize adaptive video sampling, which can further improve the quality of the reconstruction of the whole video without increasing the sampling rate. It is impossible to obtain the structural features of the pixel domain of the video signal, which makes it very challenging to study the rate control in the video compression perception framework. In this paper, a novel video compression perception distortion model is proposed first. Then, a joint optimization algorithm of sampling rate and quantization bit depth is designed by using the model, and the rate distortion is optimized by solving the rate distortion. The optimization problem realizes the optimal sampling rate and bit depth estimation in the sense of rate distortion, and then can achieve the optimal video compression perception reconstruction quality at the same time that the target rate is realized. The simulation experiment results show that the rate control algorithm proposed in this paper can better control the rate of the optical frequency compression measurement, and compared to the traditional view. The frequency compression perception system can greatly improve the rate distortion performance of the reconstructed.3) using the spatial time sparse structure feature of the video signal to study the efficient video compression perception reconstruction algorithm. In this paper, a regularized weighted base tracking denoising method is proposed, which is used to assist the current video signal support set and pixel value. A fast iterative algorithm is constructed based on the alternating direction multiplier method to solve the problem. In addition, a video compression sensing reconstruction method based on the optimal correlation model is proposed by constructing the inter frame correlation model of the video signal in the pixel domain and the measurement domain, and a new method based on the two order is constructed. The simulation results show that the algorithm can make full use of the structural features of video signals to achieve efficient reconstruction, and can provide better sampling rate distortion performance and subjective image quality.4 compared with traditional methods. Finally, this paper focuses on distributed video compression. In this paper, we first study the correlation between the current frame and the edge information frame in the framework of distributed video compression, and construct a novel model of the less sampling correlation noise. Then, on this basis, a distributed maximum likelihood dictionary training is proposed. The video compression perception system, and a system based on dictionary learning and 1L analysis reconstructing the two joint optimization, and the iterative reconstruction algorithm based on the alternating direction multiplier method. The simulation results show that the proposed algorithm can provide better reconstruction quality compared with the traditional distributed video compression perception method.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN919.81

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