Detection of transcoded HEVC videos based on in-loop filtering and PU partitioning analyses

With the increasing maturity of video editing technology, forgers are more inclined to transcode videos to High Efficiency Video Coding (HEVC) videos, as HEVC not only enables people to enjoy high-definition videos but also allows broadcasters to stream it more efficiently across networks. Therefore, to verify the originality and authenticity, it is of great significance to propose an algorithm for detecting transcoded HEVC videos. In this paper, a theoretical model of video transcoding is first constructed, and a novel transcoding detection algorithm based on In-loop Filtering and Prediction Units (PU) Partition (IFPP) is proposed. By analyzing the statistical characteristics of strong and normal filtering modes in deblocking filtering and calculating offset values in Sample Adaptive Offset (SAO) filtering, the transcoding traces in inter-coded frames can be captured. In addition, PU partition statistics are also extracted to make full use of traces in intra-coded frames. By fusing these subfeatures, the proposed IFPP feature with 17 dimensions can be obtained, which is further fed to the Support Vector Machine (SVM) classifier. Finally, experiments are conducted on datasets with various coding parameters. Results show that the proposed algorithm outperforms existing algorithms and has better robustness.     

OSS/BSS演进——OTT时代运营商新的制高点

为帮助电信运营商在与OTT运营商的竞合中赢得主动权,文章通过分析通讯行业发展趋势,结合OSS/BSS领域的最新技术能力,为运营商指明在OSS/BSS层面需要着力把控的三个重要发展方向:1)提高快速、动态响应用户需求和新商业模式的能力,2)具备与用户、合作方、网络实时交互的能力,3)简单、灵活的OSS/BSS架构体系。     

Wavelet kernel Support Vector Machines for sparse approximation

Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines （SVM）, which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target funciton with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.     

NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM

A new method called RS-MSVM （Rough Set and Multi-class Support Vector Machine） is proposed for network intrusion detection. This method is based on rough set followed by MSVM for attribute reduction and classification respectively, The number of attributes of the network data used in this paper is reduced from 41 to 30 using rough set theory. The kernel function of HVDM-RBF （Heterogeneous Value Difference Metric Radial Basis Function）, based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data. HVDM-RBF and one-against-one method are applied to build MSVM. DARPA （Defense Advanced Research Projects Agency） intrusion detection evaluating data were used in the experiment. The testing results show that our method outperforms other methods mentioned in this paper on six aspects： detection accuracy, number of support vectors, false positive rate, falsc negative rate, training time and testing time.     

支持向量机算法及应用

系统介绍了统计学习理论和支持向量机的基本思想，总结、比较了二分类和多分类2种情况下支持向量机的主要训练算法。与人工神经网络相比，分析了支持向量机的优点。归纳了支持向量机在模式识别、函数逼近、时间序列预测、故障预测和识别、信息安全、电力系统以及电力电子中的应用。     

一种合成孔径雷达图像特征提取与目标识别的新方法

该文提出了一种利用小波域主成分分析和支持向量机进行的合成孔径雷达图像特征提取与目标识别的新方法。该方法对图像小波分解后提取低频子带图像的主成分分量作为目标的特征,利用支持向量机进行分类完成目标识别。实验结果表明,该方法可以明显提高目标的正确识别率,是一种有效的合成孔径雷达图像特征提取和目标识别方法。     

基于信号幅相特性的副瓣跟踪自动识别方法

传统雷达测角系统副瓣跟踪识别方法主要根据副瓣信号幅度较主瓣明显偏低这一特性提出,该文通过对副瓣跟踪原理的理论分析、仿真分析与实测验证,得出了关于副瓣跟踪特性的新结论：副瓣跟踪时和差相位正交、差和幅度比相对主瓣跟踪明显增大。在此基础上提出一种基于信号幅相特性的副瓣跟踪识别方法：构造以差和幅度比、和差相位差为描述子的幅相特征向量,采用支持向量机(SVM)对特征向量进行分类完成识别过程。实验结果表明,该文方法识别准确率高,鲁棒性、实时性、通用性强。     

基于支持向量回归的立体图像客观质量评价模型

立体图像质量评价是评价立体视频系统性能的有效途径,而如何利用人类视觉特性对立体图像质量进行有效评价是目前的研究难点。该文根据图像奇异值有较强稳定性的特点,结合立体图像的主观视觉特性,提出了一种基于支持向量回归(Support Vector Regression, SVR)的立体图像客观质量评价模型。该模型通过分析立体图像的视觉特性,提取左右图像的奇异值作为立体图像的特征信息,然后根据立体图像的不同失真类型情况对其特征进行融合,通过SVR预测得到立体图像质量的客观评价值。实验结果表明,采用该文提出的客观评价模型对立体数据测试库进行评价,Pearson线性相关系数值在0.93以上,Spearman等级相关系数值在0.94以上,均方根误差值接近6,异常值比率值为0.00%,符合人眼视觉特性,能够很好地预测人眼对立体图像的主观感知。     

基于稀疏最小二乘支持向量回归的非线性自适应波束形成

该文基于最小二乘支持向量回归(LS-SVR)模型提出一种非线性自适应波束形成算法,以提高模型失配、小样本数、复杂多干扰等情况下的自适应波束形成器的鲁棒性。推导了高维矩阵逆矩阵求解的递推快速算法,实现了回归参数的实时求解。采用奇异性准则实时寻找输入样本集的具有较小信息冗余度的子集,并在该子集上完成波束形成计算,使得LS-SVR波束形成的求解得以稀疏化,提高了学习效率,降低了计算复杂度与系统存储空间需求。对比仿真结果验证了所提算法的正确性和有效性。     

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.