基于壓縮感知的視頻重構方法研究
發(fā)布時間：2018-07-30 06:46
【摘要】：壓縮感知理論提供了將模擬信號直接采樣壓縮為數(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,信號的采樣和壓縮同時以遠低于奈奎斯特采樣率的極低速率進行,顯著地降低了數(shù)據(jù)采集、存儲和傳輸代價,以及信號處理時間和計算成本,具有重要的軍用和民用價值�；趬嚎s感知的視頻采集與重構,所需測量值的數(shù)目遠小于傳統(tǒng)采樣方法獲得的數(shù)據(jù)量,因而可以大幅降低信號的采樣和存儲成本,從而降低對采集器件的要求和實現(xiàn)難度;同時可以在視頻信號采集的同時實現(xiàn)壓縮,大大降低了編碼器復雜度,減少了對內存和運算資源的需求,使得在資源受限環(huán)境中可以實現(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用于視頻信號,往往會因為傳統(tǒng)的正交變換系數(shù)的稀疏性很難達到壓縮感知重構的要求而導致較差的重構質量;并且由于傳統(tǒng)的壓縮感知重構算法僅考慮了信號的稀疏性特征,并未考慮到信號自身的其他結構特征,而使其很難達到最優(yōu)的視頻重構質量。由于視頻信號區(qū)別于一般信號最大的特點是存在大量空間/時間冗余,因此如何利用相關性是研究視頻壓縮感知理論的主要難題,目前在國內外仍處于研究的起步階段。本文以壓縮感知理論為基礎,在保證視頻采樣終端低復雜度的前提下,以提高視頻壓縮感知重構質量為目的,圍繞著視頻信號的高效稀疏重構展開研究,旨在傳統(tǒng)的壓縮感知框架下通過引入視頻信號空時稀疏的結構特征,將傳統(tǒng)的非自適應結構變?yōu)樽赃m應的視頻壓縮感知框架,以減少編碼測量所需的采樣率并提高視頻重構質量。本文以國家自然科學基金、高等學校博士學科點專項科研基金等項目為研究平臺,對視頻壓縮感知重構的關鍵技術進行研究。全文內容主要針對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、解碼端高效重構算法以及分布式框架下視頻壓縮感知重構等四個方面展開研究,具體概括為:1)針對一般信號,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用。由于傳統(tǒng)壓縮感知理論的約束等距性很難在實際應用中被驗證,故本文主要在相關性判別理論框架下研究有支撐集的壓縮感知重構問題。本文在理論上證明了如果預測支撐集能夠在精度與尺寸上滿足一定條件,那么利用加權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)的重構誤差限。2)在視頻壓縮感知框架下研究速率控制算法,以實現(xiàn)自適應的視頻采樣,從而能夠在不增加采樣率的前提下進一步提高視頻整體的重構質量。具體來說,由于采樣終端無法得到視頻信號像素域的結構特征,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;然后利用該模型設計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)化問題實現(xiàn)率失真意義下最優(yōu)的采樣率與比特深度估計,進而能夠在實現(xiàn)目標碼率的同時得到最優(yōu)的視頻壓縮感知重構質量。仿真實驗結果表明,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)可大幅提高重構的率失真性能。3)利用視頻信號空時稀疏的結構特征,研究高效的視頻壓縮感知重構算法。具體來說,本文提出了一種正則化的加權基追蹤去噪重構方法,通過預測視頻信號的支撐集和像素值來輔助當前幀重構,并且基于交替方向乘子法構造了一種快速的迭代算法以實現(xiàn)該問題的求解。此外,本文通過分別構造視頻信號在像素域與測量域的幀間相關模型,提出了一種基于最優(yōu)相關模型的視頻壓縮感知重構方法,并構造了一種基于二階Bregman分裂的迭代算法來實現(xiàn)該優(yōu)化問題的求解。仿真結果表明,本文算法能夠通過充分利用視頻信號的結構特征實現(xiàn)高效重構,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量。4)本文最后重點針對分布式視頻壓縮感知,研究討論視頻重構相關的問題。具體來說,本文首先在分布式視頻壓縮感知框架下研究當前幀與邊信息幀的相關性,并構造了一種新穎的欠采樣相關噪聲模型。然后,在此基礎上,本文提出了一種基于最大似然字典訓練的分布式視頻壓縮感知系統(tǒng),和一種基于字典學習和1l分析重構二者聯(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算法。仿真實驗結果表明,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質量。
[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.
【學位授予單位】：西安電子科技大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN919.81
本文編號：2154171
[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.
【學位授予單位】：西安電子科技大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN919.81
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