Packet loss resilience using unequal forward error correction assignment for video transmission over communication networks

Forward error correction (FEC) methods have been developed for packet loss resilience in application layer for real-time video transmission over communication networks. In this paper, an efficient packet loss resilience method is proposed using closed form solution for unequal FEC assignment based on a new packet distortion model. We first derive the packet distortion model by investigating the error concealment property and error propagation effect in H.264. To select the source and channel rate minimizing the overall distortion, we present a model-based rate allocation algorithm using the packet distortion model and rate-distortion function. Then we propose the closed form solution for unequal FEC assignment, which uses the packet distortion model and considers channel status information. Simulation results show that the proposed method gives substantial improvement for the received video quality in packet-lossy Internet and wireless network environments, while it requires much less computational complexity compared to the previous scheme.     

Transmission distortion estimation for real-time video delivery over hybrid channels with bit errors and packet erasures

We study transmission distortion of video delivery over hybrid channels with bit errors and packet erasures. Hybrid channels have been prone to suffering from transmission error in the forms of packet erasures due to congestion at wired networks, as well as bit errors caused by the wireless interference or fading on wireless links. We present a recursion approach to transmission distortion estimation over hybrid channels in successive frames with the prediction mode. This approach is feasible for video transmission applications as it is capable of online estimation of transmission distortion on received decoded video. Our extensive simulation results on six well-known video sequences demonstrate that our proposed approach is accurate and robust.     

A new measure of index assignment in dynamic channels

In dynamic noisy communication channels, a channel bit error rate (BER) varies with time. When channel distortion measure D_c is a function of bit error rate, an optimal index assignment to minimize D_c is not as uniformly optimal as BER varies. The paper presents a new distortion measure called expected channel distortion (ECD). It is independent of the bit error rate by two proposed beta-type distributions in a multiple-error pattern. Additionally, any index assignment algorithm with ECD measure is mismatch-free. In other words, index assignment algorithms can be against channel mismatch even though BER varies with time in dynamic channels. The voice digitization in North American Telephone Systems (CCITT) is adopted to demonstrate the usefulness of the proposed measure.     

信道估计误差下多用户MIMO／OFDM下行系统自适应传输

以系统总速率最大化为目标，提出在非理想信道信息情形下基于空分复用的多用户MIMO／OFDM下行系统的自适应资源分配方案，并满足用户的QoS要求。该方案首先根据用户信道估计误差模型和用户QoS对用户进行子载波分配，然后在各个子载波上进行功率和比特分配。仿真结果表明，该方案相对静态分配方案可获得更大的总速率，而且对信道估计错误更加强健，可应用到信道信息不确定的实际系统中。     

Media Flow Rate Allocation in Multipath Networks

We address the problem of joint path selection and source rate allocation in order to optimize the media specific quality of service in streaming of stored video sequences on multipath networks. An optimization problem is proposed in order to minimize the end-to-end distortion, which depends on video sequence dependent parameters, and network properties. An in-depth analysis of the media distortion characteristics allows us to define a low complexity algorithm for an optimal flow rate allocation in multipath network scenarios. In particular, we show that a greedy allocation of rate along paths with increasing error probability leads to an optimal solution. We argue that a network path shall not be chosen for transmission, unless all other available paths with lower error probability have been chosen. Moreover, the chosen paths should be used at their maximum available end-to-end bandwidth. Simulation results show that the optimal flow rate allocation carefully adapts the total streaming rate and the number of chosen paths, to the end-to-end transmission error probability. In many scenarios, the optimal rate allocation provides more than 20% improvement in received video quality, compared to heuristic-based algorithms. This motivates its use in multipath networks, where it optimizes media specific quality of service, and simultaneously saves network resources at the price of a very low computational complexity.     

基于视频通信的信道码码率分配方法

针对差错信道下视频信源的不等差错保护问题,设计一个新的信道码码率分配策略,对一个图片组中不同位置的帧给予不同强度的保护。该策略考虑了视频信源的误码扩散问题,分析了差错掩盖方法与信道失真计算的关系,通过在编码端有效估计信道差错所引入的失真并建立最优码率分配问题,利用遗传算法快速得到最优的信道码码率分配方案。实验采用H.264视频编码标准,结果证明该策略能较好地提升视频传输系统性能。     

Bayesian on-line spectral change point detection: a soft computing approach for on-line ASR

Current automatic speech recognition (ASR) works in off-line mode and needs prior knowledge of the stationary or quasi-stationary test conditions for expected word recognition accuracy. These requirements limit the application of ASR for real-world applications where test conditions are highly non-stationary and are not known a priori. This paper presents an innovative frame dynamic rapid adaptation and noise compensation technique for tracking highly non-stationary noises and its application for on-line ASR. The proposed algorithm is based on a soft computing model using Bayesian on-line inference for spectral change point detection (BOSCPD) in unknown non-stationary noises. BOSCPD is tested with the MCRA noise tracking technique for on-line rapid environmental change learning in different non-stationary noise scenarios. The test results show that the proposed BOSCPD technique reduces the delay in spectral change point detection significantly compared to the baseline MCRA and its derivatives. The proposed BOSCPD soft computing model is tested for joint additive and channel distortions compensation (JAC)-based on-line ASR in unknown test conditions using non-stationary noisy speech samples from the Aurora 2 speech database. The simulation results for the on-line AR show significant improvement in recognition accuracy compared to the baseline Aurora 2 distributed speech recognition (DSR) in batch-mode.     

Cross-Layer prioritized H.264 video packetization and error protection over noisy channels

Video transmission over wireless channels is affected by channel-induced packet losses. Distortion due to channel errors can be alleviated by applying forward error correction. Aggregating H.264/AVC slices to form video packets with sizes adapted to their importance can also improve transmission reliability. Larger packets are more likely to be in error but smaller packets require more overhead. We present a cross-layer dynamic programming (DP) approach to minimize the expected received video distortion by jointly addressing the priority-adaptive packet formation at the application layer and rate compatible punctured convolutional (RCPC) code rate allocation at the physical layer for prioritized slices of each group of pictures (GOP). Some low priority slices are also discarded to improve protection to more important slices and meet the channel bit-rate limitations. We propose two schemes. Our first scheme carries out joint optimization for all slices of a GOP at a time. The second scheme extends our cross-layer DP-based approach to slices of each frame by predicting the expected channel bit budget per frame for live streaming. The prediction uses a generalized linear model developed over the cumulative mean squared error per frame, channel SNR, and normalized compressed frame bit budget. The parameters are determined over a video dataset that spans high, medium and low motion complexity. The predicted frame bit budget is used to derive the packet sizes and corresponding RCPC code rates for live transmission using our DP-based approach. Simulation results show that both proposed schemes significantly improve the received video quality over contemporary error protection schemes.     

基于小波分解的网络流量时间序列建模与预测

提出一种基于小波分解的网络流量时间序列的分析和预测方法。将非平稳的网络流量时间序列通过小波分解成为多个平稳分量,采用自回归滑动平均方法分别对各平稳分量进行建模,将所有分量的模型进行组合,得到原始非平稳网络流量时间序列的预测模型。在仿真实验中,利用网络流量文库的时间序列数据建立了预测模型,并对其进行独立测试检验。仿真结果表明,本预测方法提高了网络流量时间序列的预测准确率,是一种有效、稳健的网络流量预测方法。     

一种非定标图像高精度三维重建算法

由非定标图像重建三维场景有着广泛的应用。给出了一种非定标多视图像三维重建算法。该算法主要基于因子分解和光束法平差技术。首先用因子分解方法得到射影空间下相机投影矩阵和物点坐标,以旋转矩阵的正交性以及对偶绝对二次曲面秩为3为约束,将射影空间升级到欧式空间,最后用光束法平差进行优化。该方法可同时获得相机的内外参数、畸变系数和场景的三维坐标。仿真实验表明,在1000 mm×1000 mm×400mm的范围内,当像点检测误差在0-1pixel和0-2pixel内,所重建三维点的误差分别为0.1530 mm和0.6712 mm。在500 mm×500 m×200 mm下,真实实验重构三维点的误差在0.3 mm以内。所提出的算法稳定可靠,可对实际工程进行指导。     

基于MMSE-LSA语音增强算法在非平稳环境下的研究与实现

讨论了非平稳环境下基于语音短时对数谱的最小均方误差(MMSE-LSA)估计的语音增强算法.众所周知,语音信号为时变信号,在假设语音频谱分布为高斯分布的前提下,实验的工作重点是将MMSE-LSA算法与其它语音增强算法(以谱相减的语音增强为例)比较.实验结果表明:该MMSE-LSA算法的语音增强效果很好,特别是在信噪比低时的非平稳环境下效果更为明显.     

An adaptive NR filter for spectrum analysis

An adaptive non-recursive (NR) filter with application to spectrum analysis is presented. The adaptive nature is implemented with a weight adjustment algorithm (LMS) on the filter that performs a steepest descent minimization of mean square error. The difference between an “interference” or coherent representative of it and the raw signal under analysis via its spectral content is defined as the error. A priori knowledge of the disturbances consists of the spectral content of the disturbances without regard to amplitude or phase information.The algorithm is manifest with a digital arithmetic unit using high speed logic in a robust 512 weight digital filter fabricated with current integrated circuit technology. The experimental configuration is described along with improvements currently attainable with advancements in technology. Applications to spectrum analysis, whereby unknown disturbances degrade interpretation of spectrum signatures in bearing studies, acoustic phenomena, and sonar signal analysis, is described.Adaptive filter behavior with stationary as well as non-stationary input signals are presented. It is shown that for some narrowband as well as broadband disturbances the filter tracks and adaptively rejects disturbance in real-time so as to present uncluttered renditions of the spectrum signatures or spectrograms for analysis.     

Robust image and video transmission over spectrally shaped channelsusing multicarrier modulation

This paper presents a new parallel transmission framework for reliable multimedia data transmission over spectrally shaped channels using multicarrier modulation. We propose to transmit source data layers of different perceptual importance in parallel, each occupying a number of subchannels. New loading algorithms are developed to efficiently allocate the available resources, e.g., transmitted power and bit rate, to the subchannels according to the source layers they transmit. Instead of making the bit error rate of all the subchannels equal as in most existing loading algorithms, the proposed algorithm assigns different error performance to the subchannels to achieve unequal error protection for different layers. The channel induced distortion in mean-square sense is minimized. We show that the proposed system can be applied nicely to both fixed length coding and variable-length coding. Asymptotic gains with respect to channel distortion are also derived. Numerical examples show that the proposed algorithm achieves significant performance improvement compared to the existing work, especially for spectrally shaped channels commonly used in in ADSL systems     

一种基于信源信道联合的最优速率分配算法

针对视频数据在无线信道上可靠传输问题,提出了一种基于信源信道联合的最优速率分配算法。该算法在网络带宽一定的情况下,从信源、信道及差错弹性能力权衡考虑,引入了信源解码器的抗误码性能指标,根据不同的信道状态确定信源信道编码的最优速率分配方案,从而获得最大的可解码长度,并最终获取最佳重建视频质量。仿真结果表明,该方案与传统的联合信源信道速率分配算法相比可获得更高的性能增益,适合于视频数据在无线网络上传输。     

基于小波半规则网格压缩均方误差的近似方法

为了实现数据量大的图像在网络上的快速传输,就必须对其进行高压缩。现在一般采用比较先进的小波压缩方法,但用压缩后的数据重构得到的图像与原图像相比有明显的差别。为了降低这种失真,开始部分提出了一种有效的基于位分配模型的性能优化方法;为了实现这种快速和低复杂度的分配方法,提出了和半规则网格几何编码相关的重建均方误差的近似方法。并用实验结果说明,在基于模型分配的过程中将上述近似方法得到的结果作为失真可以改善小波编码器的性能,提高编码增益,从而达到在不降低图像压缩率下提高图像质量的目的。     

一种通用的视频传输端到端失真度估算方法

在综合考虑差错扩散、差错掩盖策略及信道误码率的基础上,从像素级对端到端的视频传输差错扩散失真情况进行了分析,根据帧间相关系数和掩盖比率,提出了一种适用于各种时域差错掩盖方法的通用传输失真度估算模型.基于包丢失的仿真实验表明,该模型能够较好地近似估计出差错扩散失真情况,可以适应于各种不同的时域掩盖方法.实验还显示,基于该模型的宏块编码模式的帧内更新选择,与传统的R-D模式帧内更新算法相比,达到了较好地抑制差错扩散的效果,可适用于多种时域掩盖方法.     

Robust power allocation algorithm for analog network coding with imperfect CSI

Power allocation promises significant benefits in wireless networks. However, these benefits depend on knowledge of the channel state information （CSI）, which is hardly perfect. Therefore, robust algorithms that take into account such CSI uncertainties play an important role in the design of practical systems. In this paper, we formulate the power allocation problem as the maximum individual outage probability minimization subject to total power consumption for analog network coding （ANC） protocol of a two-way relay system. We show that these problems can be cast as convex optimization problems. Non-robust power allocation algorithm is first developed under the ideal assumption of perfect CSI. Then we introduce robust optimization methodology that accounts for the imperfect CSI. We show that ignoring CSI uncertainties in our designs can lead to drastic performance degradation. On the other hand, the proposed robust power allocation provides significant performance gain over non-robust power allocation and uniform power allocation in terms of overall system outage probability over a wide range of channel estimation errors. This work highlights the importance of the proposed robust algorithm in realistic two-way relaying networks.     

Robust linear filtering for discrete-time hybrid Markov linear systems

In this paper we consider the robust linear filtering of hybrid discrete-time Markovian jump linear systems. We assume that only an output of the system is available, and therefore the values of the jump parameter are not known. It is desired to design a dynamic linear filter such that the closed loop system is mean square stable and minimizes the stationary expected value of the square error. We consider uncertainties on the parameters of the possible modes of operation of the system. A linear matrix inequalities (LMI) formulation is proposed to solve the problem. For the case in which there are no uncertainties on the modes of operation of the system, we show that the LMI formulation provides a filter with the same stationary mean square error as the one obtained from the Riccati equation approach.     

基于非平稳时频分析的无线网络信道均衡算法

在无线网络数据通信中,由于阵元码间干扰导致信道具有非平稳特性,产生信道失衡效应,需要进行信道均衡设计,提高无线网络的数据传输性能.传统方法采用Hilbert变换扩频方法进行通信信道均衡,通过Hilbert变换使离散数据解析化,由于信道多径扩展导致码间干扰,为了提高信道均衡效果,提出一种基于非平稳时频分析的无线网络信道均衡算法.构建无线网络数据传输的空间多径信道模型,采用时频分析方法进行通信信号码间干扰抑制处理,采用提取的时频特征对通信信道进行正交频分复用分解,采用时频特征分析方法,对相位偏移进行空间码元重组,去掉路径相移偏量,实现信道均衡.仿真结果表明,采用该算法具有较好的信道均衡性能,能有效抑制通信信道的码间干扰,降低无线网络数据通信的误比特率,改善通信质量.     

基于交叉注意力机制的波束形成后置滤波网络

针对经典后置滤波器存在的对非平稳噪声抑制效果较差且存在目标语音失真的问题,提出一种基于交叉注意力机制的后置滤波网络,使用基于门控循环单元的编解码器作为网络框架,并在编解码器组之间添加残差连接;使用基于伽马通域的波束输出信号与噪声参考信号功率谱的子带增益作为双特征输入;使用特征交叉的多头归一化点积注意力捕获序列输入的长距离依赖信息并进行特征融合。实验结果表明,该算法在不同信扰比和噪声条件下的语音质量和可懂度指标均优于基线系统,具有较强的鲁棒性;在对非平稳噪声具有较好抑制效果的同时,能最小化目标语音的失真;且相较端到端的深度学习方法,具有轻量化和低时延的特点,能满足实际工程应用的需求。