广义高斯信源的自适应编码研究

首先介绍了当前最新的几种视频编码标准并且进行了比较,熵编码是每一个视频编码标准必须认真研究的课题,为了减少由于失配所带来的效率损失,本文提出了一种自适应编码技术:自适应指数哥伦布码,并与自适应算术编码进行了比较.分析和仿真都表明即使信源特性在大范围内发生变化,自适应指数哥伦布码对于保持高的编码效率是足够稳健的(90%以上的情况),同时保持了指数哥伦布码和哥伦布-莱期码的简洁性.     

Progressive Coding of a Gaussian Source Using Matching Pursuit

In this paper, the application of matching pursuit (MP) in progressive coding of memoryless Gaussian sources is studied. In addition, a detailed analysis of the rate-distortion performance of our proposed MP encoder is presented, and the distortion of the MP encoder is derived in terms of dictionary size and number of quantization levels and the optimum parameters are calculated. Our analysis is based on a probabilistic model for matching pursuit residual vectors. Our simulation results verify the accuracy of our analysis and show that matching pursuit can produce an embedded bitstream with comparable quality to existing quantizers. The MP encoder outperforms quantizers that are capable of producing embedded bitstreams.     

Source and Channel Coding for Correlated Sources Over Multiuser Channels

Source and channel coding over multiuser channels in which receivers have access to correlated source side information are considered. For several multiuser channel models necessary and sufficient conditions for optimal separation of the source and channel codes are obtained. In particular, the multiple-access channel, the compound multiple-access channel, the interference channel, and the two-way channel with correlated sources and correlated receiver side information are considered, and the optimality of separation is shown to hold for certain source and side information structures. Interestingly, the optimal separate source and channel codes identified for these models are not necessarily the optimal codes for the underlying source coding or the channel coding problems. In other words, while separation of the source and channel codes is optimal, the nature of these optimal codes is impacted by the joint design criterion.      

一种分布式信源编码中最优量化器的设计

针对传统的向量量化压缩的不足性,提高量化效果的方法主要是增加向量的维数,但同时增加了计算的复杂性.为了解决分布式编码中量化器的设计问题,在联合条件熵约束的条件下,着重从WZ量化器的最优条件分析,提出了一种Lloyd迭代算法,通过实验证明,该算法比传统的算法更可以保证量化器的局部最优性.     

On Practical Design for Joint Distributed Source and Network Coding

This paper considers the problem of communicating correlated information from multiple source nodes over a network of noiseless channels to multiple destination nodes, where each destination node wants to recover all sources. The problem involves a joint consideration of distributed compression and network information relaying. Although the optimal rate region has been theoretically characterized, it was not clear how to design practical communication schemes with low complexity. This work provides a partial solution to this problem by proposing a low-complexity scheme for the special case with two sources whose correlation is characterized by a binary symmetric channel. Our scheme is based on a careful combination of linear syndrome-based Slepian-Wolf coding and random linear mixing (network coding). It is in general suboptimal; however, its low complexity and robustness to network dynamics make it suitable for practical implementation.      

一种分簇无线传感器网络中的分布式信源编码算法

针对密集型无线传感器网络中信息存在大量冗余的问题,该文提出了一种适用于分簇无线传感网络的分布式信源编码算法。该算法以边信息作为初始参考信源,利用信源间的相关性来决定各信源的编码顺序和参考信源,然后由各信源相对于参考信源进行相关编码,接收端则根据编码顺序和参考信源进行相关译码。该文同时针对模值编码的方式,给出了一种低复杂度的译码算法。理论分析和仿真结果表明,将该算法应用于分簇路由协议中可以有效地降低节点的发送比特数,从而降低网络的能耗以延长网络寿命。     

Distributed Source Coding in the Presence of Byzantine Sensors

The distributed source coding problem is considered when the sensors, or encoders, are under Byzantine attack; that is, an unknown group of sensors have been reprogrammed by a malicious intruder to undermine the reconstruction at the fusion center. Three different forms of the problem are considered. The first is a variable-rate setup, in which the decoder adaptively chooses the rates at which the sensors transmit. An explicit characterization of the variable-rate achievable sum rates is given for any number of sensors and any groups of traitors. The converse is proved constructively by letting the traitors simulate a fake distribution and report the generated values as the true ones. This fake distribution is chosen so that the decoder cannot determine which sensors are traitors while maximizing the required rate to decode every value. Achievability is proved using a scheme in which the decoder receives small packets of information from a sensor until its message can be decoded, before moving on to the next sensor. The sensors use randomization to choose from a set of coding functions, which makes it probabilistically impossible for the traitors to cause the decoder to make an error. Two forms of the fixed-rate problem are considered, one with deterministic coding and one with randomized coding. The achievable rate regions are given for both these problems, and it is shown that lower rates can be achieved with randomized coding.     

Distributed Joint Source-Channel Coding for Correlated Sources Using Non-systematic Repeat-Accumulate Based Codes

In this paper, we propose a technique for coding the data from multiple correlated binary sources, with the aim of providing an alternative solution to the correlated source compression problem. Using non-systematic repeat-accumulate based codes, it is possible to achieve compression which is close to the Slepian–Wolf bound without relying on massive puncturing. With the technique proposed in this paper, instead of puncturing, compression is achieved by increasing check node degrees. Hence, the code rate can be more flexibly adjusted with the proposed technique in comparison with the puncturing-based schemes. Furthermore, the technique is applied to distributed joint source-channel coding (DJSCC). It is shown that in many cases tested, the proposed scheme can achieve mutual information very close to one with the lower signal-to-noise power ratio than turbo and low density generator matrix based DJSCC in additive white Gaussian noise channel. The convergence property of the system is also evaluated via the extrinsic information transfer analysis.     

Hierarchical Distributed Source Coding Scheme and Optimal Transmission Scheduling for Wireless Sensor Networks

Distributed source coding (DSC) can be used to compress multiple correlated sensor measurements. These sensors send their compressed data to a central station for joint decoding. However, the issue on designing an optimal transmission scheduling scheme of DSC packets for WSNs have not been well addressed in the literature. In this work, we proposed a novel DSC coding scheme—hierarchical coding scheme, which exploits inter-node coding dependency in sensing-driven and correlated manner. In addition, the interaction between hierarchical coding topology and transmission is considered. We optimize the transmission schedule of DSC nodes to achieve better decoding quality. Our approach can be practically applied to any WSN topologies with correlated source coding nodes. Simulation shows that our work can achieve higher decoding accuracy and compression rate than previous approaches, and the decoding accuracy would not have much degradation under the error-prone wireless environment.     

Source Coding With Feed-Forward: Rate-Distortion Theorems and Error Exponents for a General Source

In this work, we consider a source coding model with feed-forward. We analyze a system with a noiseless, feed-forward link where the decoder has knowledge of all previous source samples while reconstructing the present sample. The rate-distortion function for an arbitrary source with feed-forward is derived in terms of directed information, a variant of mutual information. We further investigate the nature of the rate-distortion function with feed-forward for two common types of sources- discrete memory- less sources and Gaussian sources. We then characterize the error exponent for a general source with feed-forward. The results are then extended to feed-forward with an arbitrary delay larger than the block length.     

空时网格码的联合迭代最大似然估计和译码

该文提出了一种适合空时网格码的联合迭代最大似然估计和译码的方法,利用较少的导频,能够获得比传统非迭代最大似然译码更好的系统性能,仿真结果显示经过4次迭代,其性能与接收机已知理想信道信息下的最大似然译码比较只有不到1dB的损失,最后根据仿真结果给出一个导频数目和迭代次数的合理选择。     

Optimality of KLT for High-Rate Transform Coding of Gaussian Vector-Scale Mixtures: Application to Reconstruction, Estimation, and Classification

The Karhunen-Loeacuteve transform (KLT) is known to be optimal for high-rate transform coding of Gaussian vectors for both fixed-rate and variable-rate encoding. The KLT is also known to be suboptimal for some non-Gaussian models. This paper proves high-rate optimality of the KLT for variable-rate encoding of a broad class of non-Gaussian vectors: Gaussian vector-scale mixtures (GVSM), which extend the Gaussian scale mixture (GSM) model of natural signals. A key concavity property of the scalar GSM (same as the scalar GVSM) is derived to complete the proof. Optimality holds under a broad class of quadratic criteria, which include mean-squared error (MSE) as well as generalized f-divergence loss in estimation and binary classification systems. Finally, the theory is illustrated using two applications: signal estimation in multiplicative noise and joint optimization of classification/reconstruction systems     

Remote Vector Gaussian Source Coding With Decoder Side Information Under Mutual Information and Distortion Constraints

Let X , Y , Z be zero-mean, jointly Gaussian random vectors of dimensions n_x, n_y, and n_z, respectively. Let P be the set of random variables W such that W harr Y harr (X, Z) is a Markov string. We consider the following optimization problem: _WisinP ^min I(Y; Z) subject to one of the following two possible constraints: 1) I(X; W|Z) ges R_I, and 2) the mean squared error between X and Xcirc = E(X|W, Z) is less than d . The problem under the first kind of constraint is motivated by multiple-input multiple-output (MIMO) relay channels with an oblivious transmitter and a relay connected to the receiver through a dedicated link, while for the second case, it is motivated by source coding with decoder side information where the sensor observation is noisy. In both cases, we show that jointly Gaussian solutions are optimal. Moreover, explicit water filling interpretations are given for both cases, which suggest transform coding approaches performed in different transform domains, and that the optimal solution for one problem is, in general, suboptimal for the other.     

Source and Channel Coding for Cooperative Relaying

User cooperation is a powerful tool to combat fading and increase robustness for communication over wireless channels. Although it is doubtless a promising technique for enhancing channel reliability, its performance in terms of average source distortion is not clear since source-channel separation theorem fails under the most common nonergodic slow-fading channel assumption, when channel state information (CSI) is only available at the receiving terminals. This work sheds some light on the end-to-end performance of joint source-channel coding for cooperative relay systems in the high signal-to-noise ratio (SNR) regime. Considering distortion exponent as a figure of merit, we propose various strategies for cooperative source and channel coding that significantly improve the performance compared to the conventional scheme of source coding followed by cooperative channel coding. We characterize the optimal distortion exponent of a full-duplex relay channel for all bandwidth ratios. For the half-duplex relay channel, we provide an upper bound which is tight for small and large bandwidth ratios. We consider the effect of correlated side information on the distortion exponent as well.     

Successive Refinement Via Broadcast: Optimizing Expected Distortion of a Gaussian Source Over a Gaussian Fading Channel

We consider the problem of transmitting a Gaussian source on a slowly fading Gaussian channel, subject to the mean-squared error distortion measure. The channel state information is known only at the receiver but not at the transmitter. The source is assumed to be encoded in a successive refinement (SR) manner, and then transmitted over the channel using the broadcast strategy. In order to minimize the expected distortion at the receiver, optimal power allocation is essential. We propose an efficient algorithm to compute the optimal solution in linear time , when the total number of possible discrete fading states. Moreover, we provide a derivation of the optimal power allocation when the fading state is a continuum, using the classical variational method. The proposed algorithm as well as the continuous solution is based on an alternative representation of the capacity region of the Gaussian broadcast channel.     

Statistics of 2-D DT-CWT Coefficients for a Gaussian Distributed Signal

This paper deals with the statistical properties of the two-dimensional dual-tree complex wavelet transform (DT-CWT) coefficients of a Gaussian distributed signal both in the Cartesian and polar forms. The first level of decomposition of the DT-CWT uses the wavelet filters that form only an approximate Hilbert-pair, while those at the higher levels form almost an exact Hilbert-pair. Hence, a significant correlation exists between the quadrature-filtered coefficients of the two trees in the first level of decomposition as compared to the other levels. As a consequence, in the Cartesian representation, the real and imaginary components of the complex coefficients are modeled as independent zero-mean Gaussian having unequal variances for the first level of decomposition and equal variances for the higher levels. In the polar representation, the magnitude components are modeled by a generalized Gamma probability density function (PDF) for the first-level decomposition and a Rayleigh PDF for the higher levels. The corresponding phase components are modeled by an analytic PDF. The Monte Carlo simulations show that the proposed PDFs of the transform coefficients match very well with the empirical ones. It is shown that the moments of the corresponding PDFs closely approximate the estimated sample moments. Finally, two techniques, namely, maximum a posteriori-based estimation and phase-based ridge detection are developed using the proposed PDFs. Simulation studies are carried out showing that the use of the proposed techniques provides improved estimation and detection performance of images in a noisy environment.      

Coding Theorems on the Threshold Scheme for a General Source

In this paper, coding theorems on the (t, m) -threshold scheme for a general source are discussed, where m means the number of the shares and t means a threshold. The (t,m) -threshold scheme treated in this paper encrypts n source outputs Xⁿ to m shares at once and is required to satisfy the two conditions that 1) Xⁿ is reproduced from arbitrary t shares, and 2) almost no information of Xⁿ is revealed from any t - 1 shares. It is shown that the (t,m) -threshold scheme must satisfy certain inequalities including the limit inferiors in probability. One of the inequalities is closely related to the minimum length of the fair random bits needed to a dealer for realizing the (t, m) -threshold scheme. In addition, it is shown that a certain variation of Shamir's threshold scheme meets the two conditions. The same approach can be taken to the problems of Shannon's cipher system with the perfect secrecy and fixed-length source coding with vanishing decoding error probability. It is shown that the same kind of inequalities, which indicate the converse coding theorems, hold in both two cases.