基于压缩感知的多描述编码研究
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摘要
随着信息技术的快速发展，作为信息的主要载体图像和视频信号容量日益增大成为网络数据的主体。当前的通信网络拥有两大特点：一方面通信网络的信道具有多变的特点，而另一方面网络终端信息采集设备却日趋迷你化，这给大数据量的信号编码任务提出了双重挑战。编码简单、性能鲁棒的面向网络编码方式成为信息编码领域一个重要的研究课题。传统的面向网络编码方案广泛采用先采集后压缩最后再保护的结构对信号进行编码，致使其拥有较高的编码复杂度，从而无法应用于资源有限的编码终端。近年来,压缩感知理论带给我们一个全新的编码理念。抛弃传统编码方案中在编码端进行稀疏变换的编码模式，基于压缩感知的编码系统在编码端可以忽略信号的具体稀疏特性，而采用普适的观测矩阵对信号进行信息采集，并将信号的稀疏表示任务转交给解码端处理。这一编码特性正适用于当前网络通信环境特点：编码器资源有限而解码器功能强大。因此，本文将压缩感知多描述编码作为研究对象，以抗数据错误能力和编码效率作为衡量准则，对压缩感知编码系统的量化方案，重构方案以及观测结构进行研究，旨在建立一个编码简单、性能鲁棒的高效多描述编码方案。本文的主要贡献点包括：
(1)提出一种精细粒度的压缩感知多描述视频编码方案。针对视频大数据量信号，采用具有随机性的托普利兹矩阵对信号进行观测，保持了编码器的简单性。分别利用视频信号的帧内和帧间相关性，提出一种空域分段自回归(PiecewiseAutoRegression, PAR)模型和时域差分稀疏相结合的稀疏重构算法，达到了视频信号的高质量重构，并在此基础上，将对参考帧的下采样引入到信号观测中，保证了重构模型初始参数的准确性。仿真实验表明，该编码方案可比传统非均匀错误保护方案拥有更鲁棒的抗丢包和抗误码性能。
(2)提出一种压缩感知观测系数的渐进式的量化方案。压缩感知多描述编码系统为了保持编码简单的特性，采用了均匀量化器，导致其编码效率较低。为了提升整个编码系统的编码性能，本文对观测值的相关性进行考察，揭示了随机观测值之间并不完全“随机”的事实，证实了其隐式相关性的存在。利用此相关性，构建出普通标量量化和分级量化相结合的渐进式量化方案，并将反量化问题刻画成简单的估计问题，保证了量化值的正确恢复。此外，对该系统的实现问题也进行了讨论，给出了一种简单的解决方案，保持了低编码复杂度特性。仿真实验表明，所提出的渐进式量化方案可以有效提升压缩感知编码的性能。
(3)提出一种多特征空间的重构算法。图像信号为非平稳信号，在各局部区域显现出不同的特征，因此单一的特征描述重构算法无法准确刻画信号本身。鉴于此，本文采用两种互补的特征模型对信号联合刻画，并自适应确定各自权重因子，从而实现对信号模型的最优选取。在此基础上，根据贝叶斯理论推导出权重因子的估计方法，保证了信号特征模型选择的准确性。此外，在多特征重构方法的求解算法中，采用交替方向法进行求解，克服了多目标函数同时优化所面临的计算困难。仿真实验表明，该重构方案比单特征空间重构具有更高的重构质量，在边缘纹理结构区域和平滑区域都表现出更优的主观视觉效果。
(4)提出一种混合观测的压缩感知多描述编码方案。针对随机观测系数信息熵较高，编码效率低下这一缺点，本文打破传统压缩感知多描述编码的随机观测方式，提出基于DCT观测和随机观测混合观测的多描述编码方案。通过采用哥伦布编码作为熵编码方案保证了编码的低复杂度特性。另外，利用两种观测系数之间的相关性，减小了高斯观测值的平均码长。由于同时采用了DCT和随机观测矩阵，编码系统在保证高抗丢包能力的同时，又具有较高的编码效率。仿真实验表明，同等码率同等信道条件下，该编码方案比随机观测多描述编码方案具有更高的重构质量。
As the main body of the network data, the amount of the image and video signals isincreasing rapidly with the development of information techniques. The current networkcommunication systems mainly have two features. On one hand, the communicationchannels change frequently; on the other hand, the information acquisition systemsbecome more and more miniaturized. These two features make big data encoding andtransmission very challenging. Thus, designing a simple and robust network codingschemes has become an important research topic. Traditional“sampling-compression-protection” based network coding methods have very highcoding complexity, and cannot be used for resource limited network encoders. Recently,as a new theory of sampling, Compressive sensing (CS) provides us a new solution forimage coding. Different from the traditional image encoders that transforms the animage into a sparse domain, the CS-based coding scheme compresses images withuniversal random measurement matrixes, and shifts the task of sparse representation tothe decoders. Such coding scheme is very suitable for current network communicationsystems, which have resource limited encoders and more powerful decoders. In thisthesis, aiming to developing a robust and low-complexity multiple description codingscheme, we propose a CS-based multiple description coding (MDC) method, andinvestigate the quantization, reconstruction algorithm, and measurement issues. Themain contributions of the paper include:
(1) A fine granularity CS-based multiple description video coding method has beenproposed. For a low-complexity encoder, a random partial Toeplitz matrix is adopted togenerate fine granularity descriptions. To exploit the spatial and temporal correlations,we propose to use the piecewise autoregressive (PAR) model and total variation (TV)model for effective CS image reconstruction. We also propose to generate adown-sampling based description to facilitate the learning of the varying PAR modelparameters. Compared with traditional unequal error protection (UEP) based MDCschemes, the proposed MDC coding scheme is more robust to resist both erasure and biterrors.
(2) A progressive quantization (PQ) for compressive sensing measurements isproposed. In order to maintain the low complexity of the encoder, a scalar uniform quantizer is used. To improve the coding efficiency, we investigate the distribution ofmeasurements, and find that random measurements are not “random”. Instead there areimplicit correlations between these random measurements. To exploit these correlations,a progressive quantization scheme using both scalar quantizer and binned quantizer wasproposed. The dequantization process is recast as an estimation problem to improve thereconstruction of the quantized signal. In addition, we also discussed the practicalimplementation issue and provided a simple solver for the PQ scheme to make thecoding complexity as low as possible. Experimental results show that our PQ schemecan outperform the tradition single-layer quantization (SQ) scheme in both PSNR andvisual quality.
(3) We also propose a new image reconstruction algorithm using multiple sparsespaces to further improve the reconstruction quality. Since natural images arenon-stationary, the reconstruction algorithm based on single sparse space usually fails tocharacterize the local image structures. To overcome this drawback, we adopt twocomplementary models to characterize the local sparsity of natural images, andadaptively select the better one according to the local image features. Using Bayesianestimation theory, we propose an estimation method to adaptively select theregularization parameters. In addition, an alternating direction method (ADM) is used toefficiently solve the resulted optimization problem. Experimental results show that theproposed reconstruction algorithm outperforms the method using single sparse space.
(4) In order for a low complexity CS-based coding scheme, we propose a hybridsampling scheme using DCT and random measurement matrix. The measurements areencoded by using a Golomb entropy coding scheme, which has low coding complexity.By exploiting the correlation between the two kinds of measurements, the average codelength can be further reduced. Since both DCT and random matrices are incorporatedinto the system, the coding efficiency is improved and a high robust ability to resisterasures is achieved. Experimental results show that the proposed CS-based multiplecoding scheme based on hybrid sampling is more effective than that based on randomsampling.
(1)提出一种精细粒度的压缩感知多描述视频编码方案。针对视频大数据量信号，采用具有随机性的托普利兹矩阵对信号进行观测，保持了编码器的简单性。分别利用视频信号的帧内和帧间相关性，提出一种空域分段自回归(PiecewiseAutoRegression, PAR)模型和时域差分稀疏相结合的稀疏重构算法，达到了视频信号的高质量重构，并在此基础上，将对参考帧的下采样引入到信号观测中，保证了重构模型初始参数的准确性。仿真实验表明，该编码方案可比传统非均匀错误保护方案拥有更鲁棒的抗丢包和抗误码性能。
(2)提出一种压缩感知观测系数的渐进式的量化方案。压缩感知多描述编码系统为了保持编码简单的特性，采用了均匀量化器，导致其编码效率较低。为了提升整个编码系统的编码性能，本文对观测值的相关性进行考察，揭示了随机观测值之间并不完全“随机”的事实，证实了其隐式相关性的存在。利用此相关性，构建出普通标量量化和分级量化相结合的渐进式量化方案，并将反量化问题刻画成简单的估计问题，保证了量化值的正确恢复。此外，对该系统的实现问题也进行了讨论，给出了一种简单的解决方案，保持了低编码复杂度特性。仿真实验表明，所提出的渐进式量化方案可以有效提升压缩感知编码的性能。
(3)提出一种多特征空间的重构算法。图像信号为非平稳信号，在各局部区域显现出不同的特征，因此单一的特征描述重构算法无法准确刻画信号本身。鉴于此，本文采用两种互补的特征模型对信号联合刻画，并自适应确定各自权重因子，从而实现对信号模型的最优选取。在此基础上，根据贝叶斯理论推导出权重因子的估计方法，保证了信号特征模型选择的准确性。此外，在多特征重构方法的求解算法中，采用交替方向法进行求解，克服了多目标函数同时优化所面临的计算困难。仿真实验表明，该重构方案比单特征空间重构具有更高的重构质量，在边缘纹理结构区域和平滑区域都表现出更优的主观视觉效果。
(4)提出一种混合观测的压缩感知多描述编码方案。针对随机观测系数信息熵较高，编码效率低下这一缺点，本文打破传统压缩感知多描述编码的随机观测方式，提出基于DCT观测和随机观测混合观测的多描述编码方案。通过采用哥伦布编码作为熵编码方案保证了编码的低复杂度特性。另外，利用两种观测系数之间的相关性，减小了高斯观测值的平均码长。由于同时采用了DCT和随机观测矩阵，编码系统在保证高抗丢包能力的同时，又具有较高的编码效率。仿真实验表明，同等码率同等信道条件下，该编码方案比随机观测多描述编码方案具有更高的重构质量。
As the main body of the network data, the amount of the image and video signals isincreasing rapidly with the development of information techniques. The current networkcommunication systems mainly have two features. On one hand, the communicationchannels change frequently; on the other hand, the information acquisition systemsbecome more and more miniaturized. These two features make big data encoding andtransmission very challenging. Thus, designing a simple and robust network codingschemes has become an important research topic. Traditional“sampling-compression-protection” based network coding methods have very highcoding complexity, and cannot be used for resource limited network encoders. Recently,as a new theory of sampling, Compressive sensing (CS) provides us a new solution forimage coding. Different from the traditional image encoders that transforms the animage into a sparse domain, the CS-based coding scheme compresses images withuniversal random measurement matrixes, and shifts the task of sparse representation tothe decoders. Such coding scheme is very suitable for current network communicationsystems, which have resource limited encoders and more powerful decoders. In thisthesis, aiming to developing a robust and low-complexity multiple description codingscheme, we propose a CS-based multiple description coding (MDC) method, andinvestigate the quantization, reconstruction algorithm, and measurement issues. Themain contributions of the paper include:
(1) A fine granularity CS-based multiple description video coding method has beenproposed. For a low-complexity encoder, a random partial Toeplitz matrix is adopted togenerate fine granularity descriptions. To exploit the spatial and temporal correlations,we propose to use the piecewise autoregressive (PAR) model and total variation (TV)model for effective CS image reconstruction. We also propose to generate adown-sampling based description to facilitate the learning of the varying PAR modelparameters. Compared with traditional unequal error protection (UEP) based MDCschemes, the proposed MDC coding scheme is more robust to resist both erasure and biterrors.
(2) A progressive quantization (PQ) for compressive sensing measurements isproposed. In order to maintain the low complexity of the encoder, a scalar uniform quantizer is used. To improve the coding efficiency, we investigate the distribution ofmeasurements, and find that random measurements are not “random”. Instead there areimplicit correlations between these random measurements. To exploit these correlations,a progressive quantization scheme using both scalar quantizer and binned quantizer wasproposed. The dequantization process is recast as an estimation problem to improve thereconstruction of the quantized signal. In addition, we also discussed the practicalimplementation issue and provided a simple solver for the PQ scheme to make thecoding complexity as low as possible. Experimental results show that our PQ schemecan outperform the tradition single-layer quantization (SQ) scheme in both PSNR andvisual quality.
(3) We also propose a new image reconstruction algorithm using multiple sparsespaces to further improve the reconstruction quality. Since natural images arenon-stationary, the reconstruction algorithm based on single sparse space usually fails tocharacterize the local image structures. To overcome this drawback, we adopt twocomplementary models to characterize the local sparsity of natural images, andadaptively select the better one according to the local image features. Using Bayesianestimation theory, we propose an estimation method to adaptively select theregularization parameters. In addition, an alternating direction method (ADM) is used toefficiently solve the resulted optimization problem. Experimental results show that theproposed reconstruction algorithm outperforms the method using single sparse space.
(4) In order for a low complexity CS-based coding scheme, we propose a hybridsampling scheme using DCT and random measurement matrix. The measurements areencoded by using a Golomb entropy coding scheme, which has low coding complexity.By exploiting the correlation between the two kinds of measurements, the average codelength can be further reduced. Since both DCT and random matrices are incorporatedinto the system, the coding efficiency is improved and a high robust ability to resisterasures is achieved. Experimental results show that the proposed CS-based multiplecoding scheme based on hybrid sampling is more effective than that based on randomsampling.
引文
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