针对现有Laplacian模型不能精确描述相关噪声分布,分布式视频编码(Distributed Video Coding,DVC)的率失真性能改善非常有限,文中提出一种基于高斯混合模型的分布式视频编码方法.首先分析了WZ帧与相应边信息之间相关噪声的统计特征,发...针对现有Laplacian模型不能精确描述相关噪声分布,分布式视频编码(Distributed Video Coding,DVC)的率失真性能改善非常有限,文中提出一种基于高斯混合模型的分布式视频编码方法.首先分析了WZ帧与相应边信息之间相关噪声的统计特征,发现相关噪声信息的分布并不满足某种单峰分布,然后采用高斯混合模型(Gaussian Mixture Model,GMM)对噪声系数直方图进行拟合,提出基于样本特征的EM(Expectation Maximum)算法来估计模型参数.将提出的高斯混合相关噪声模型与相应的Laplacian模型进行比较,实验结果表明前者更能精确描述相关噪声的统计特征,基于该模型的DVC率失真性能优于基于Laplacian模型的DISCOVER方案,获得的平均增益接近1dB.展开更多
Performance of Turbo-Codes in communication channels with impulsive noise is analyzed. First, mathematical model of impulsive noise is presented because it has non-Gaussian nature and is found in many wireless channel...Performance of Turbo-Codes in communication channels with impulsive noise is analyzed. First, mathematical model of impulsive noise is presented because it has non-Gaussian nature and is found in many wireless channels due to impulsive phenomena of radio-frequency interference. Then, with linear Log-MAP decoding algorithm for its low complexity, Turbo-Codes are adopted and analyzed in such communication channels. To confirm the performance of the proposed method, simulations on both static and fully interleaved flat Rayleigh fading channels with impulsive noise have been carried out. It is shown that Turbo-Codes have a better performance than the conventional methods （e.g. convolutionally coded system）.展开更多
文摘Performance of Turbo-Codes in communication channels with impulsive noise is analyzed. First, mathematical model of impulsive noise is presented because it has non-Gaussian nature and is found in many wireless channels due to impulsive phenomena of radio-frequency interference. Then, with linear Log-MAP decoding algorithm for its low complexity, Turbo-Codes are adopted and analyzed in such communication channels. To confirm the performance of the proposed method, simulations on both static and fully interleaved flat Rayleigh fading channels with impulsive noise have been carried out. It is shown that Turbo-Codes have a better performance than the conventional methods （e.g. convolutionally coded system）.
