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Xiao J.,University of Liverpool | Xiao J.,Xian Jiaotong - Liverpool University | Tillo T.,Xian Jiaotong - Liverpool University | Zhao Y.,Beijing Jiaotong University | Zhao Y.,Beijing Key Laboratory of Advanced Information Science and Network Technology
IEEE Transactions on Circuits and Systems for Video Technology | Year: 2013

Forward error correction (FEC) codes are widely studied to protect streamed video over unreliable networks. Typically, enlarging the FEC coding block size can improve the error correction performance. For video streaming applications, this could be implemented by grouping more than one video frame into one FEC coding block. However, in this case, it leads to decoding delay, which is not tolerable for real-time video streaming applications. In this paper, to solve this dilemma, a real-time video streaming scheme using randomized expanding Reed-Solomon (RS) code is proposed. In this scheme, the RS coding block includes not only the video packets of the current frame, but could also include all the video packets of previous frames in the current group of pictures. At the decoding side, the parity-check equations of the current frame are jointly solved with all the parity-check equations of the previous frames. Since video packets of the following frames are not encompassed in the RS coding block, no delay will be caused for waiting for the video or parity packets of the following frames both at encoding and decoding sides. Experimental results show that the proposed scheme outperforms other real-time error resilient video streaming approaches significantly, specifically, for the Foreman sequence, the proposed scheme could provide 1.5 dB average gain over the state-of-the-art approach for 10% i.i.d. packet loss rate, whereas for the burst loss case, the average gain is more than 3 dB.MATLAB code of this paper is available for download at http://www.mmtlab.com. © 2013 IEEE. Source


Jiang Z.-Y.,Beijing Key Laboratory of Advanced Information Science and Network Technology | Liang M.-G.,Beijing Jiaotong University
Physica A: Statistical Mechanics and its Applications | Year: 2013

The most important function of a network is for transporting traffic. Due to the low traffic capacity of network systems under the global shortest path routing, plenty of heuristic routing strategies are emerging. In this paper, we propose a heuristic routing strategy called the incremental routing algorithm to improve the traffic capacity of complex networks. We divide the routing process into N(the network size) steps and, at each step, we heuristically calculate all the routes for one source node considering both the dynamic efficient betweenness centrality and node degree information. We do extensive simulations on scale-free networks to confirm the effectiveness of the proposed incremental routing strategy. The simulation results show that the traffic capacity has been enhanced by a substantial factor at the expense of a slight lengthening in the average path. © 2013 Elsevier B.V. All rights reserved. Source


Cao G.,Beijing Jiaotong University | Cao G.,Communication University of China | Zhao Y.,Beijing Jiaotong University | Zhao Y.,State Key Laboratory of Rail Traffic Control and Safety | And 3 more authors.
IEEE Transactions on Information Forensics and Security | Year: 2014

As a retouching manipulation, contrast enhancement is typically used to adjust the global brightness and contrast of digital images. Malicious users may also perform contrast enhancement locally for creating a realistic composite image. As such it is significant to detect contrast enhancement blindly for verifying the originality and authenticity of the digital images. In this paper, we propose two novel algorithms to detect the contrast enhancement involved manipulations in digital images. First, we focus on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications. The histogram peak/gap artifacts incurred by the JPEG compression and pixel value mappings are analyzed theoretically, and distinguished by identifying the zero-height gap fingerprints. Second, we propose to identify the composite image created by enforcing contrast adjustment on either one or both source regions. The positions of detected blockwise peak/gap bins are clustered for recognizing the contrast enhancement mappings applied to different source regions. The consistency between regional artifacts is checked for discovering the image forgeries and locating the composition boundary. Extensive experiments have verified the effectiveness and efficacy of the proposed techniques. © 2005-2012 IEEE. Source


An W.,Beijing Jiaotong University | An W.,Beijing Key Laboratory of Advanced Information Science and Network Technology | Liang M.,Beijing Jiaotong University | Liang M.,Beijing Key Laboratory of Advanced Information Science and Network Technology
Neurocomputing | Year: 2013

Support vector machine (SVM) is a popular machine learning technique, and it has been widely applied in many real-world applications. Since SVM is sensitive to outliers or noises in the dataset, Fuzzy SVM (FSVM) has been proposed. Like SVM, it still aims at finding an optimal hyperplane that can separate two classes with the maximal margin. The only difference is that fuzzy membership is assigned to each training point based on its importance, which makes it less sensitive to outliers or noises to some extent. However, FSVM ignores an important prior knowledge, the within-class structure. In this paper, we propose a new classification algorithm-FSVM with minimum within-class scatter (WCS-FSVM), which incorporates minimum within-class scatter in Fisher Discriminant Analysis (FDA) into FSVM. The main idea is that an optimal hyperplane is found such that the margin is maximized while the within-class scatter is kept as small as possible. In addition, we propose a new fuzzy membership function for WCS-FSVM. Experiments on six benchmarking datasets and four artificial datasets show that our proposed WCS-FSVM algorithm can not only improve the classification accuracy and generalization ability but also handle the classification problems with outliers or noises more effectively. © 2013 Elsevier B.V. Source


Jiang Z.-Y.,Beijing Jiaotong University | Jiang Z.-Y.,Beijing Key Laboratory of Advanced Information Science and Network Technology | Liang M.-G.,Beijing Jiaotong University | Liang M.-G.,Beijing Key Laboratory of Advanced Information Science and Network Technology | Wu J.-J.,Hong Kong Polytechnic University
PLoS ONE | Year: 2013

Packets transmitting in real communication networks such as the Internet can be classified as time-sensitive or time-insensitive. To better support the real-time and time-insensitive applications, we propose a two-level flow traffic model in which packets are labeled as level-1 or level-2, and those with level-1 have higher priority to be transmitted. In order to enhance the traffic capacity of the two-level flow traffic model, we expand the global dynamic routing strategy and propose a new dynamic source routing which supports no routing-flaps, high traffic capacity, and diverse traffic flows. As shown in this paper, the proposed dynamic source routing can significantly enhance the traffic capacity and quality of time-sensitive applications compared with the global shortest path routing strategy. © 2013 Jiang et al. Source

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