Network video quality assessment based on fuzzy inference system

The objective assessment method of network video quality is a challenge, because the video quality will be distorted by various factors, including transmission and compression. In order to improve the objective method, an objective assessment method based on fuzzy inference system of Mamdani is proposed. Firstly, six quality parameters are introduced. All the quality parameters are inputted to fuzzy logic controller system. Secondly, the outputs are used as next inputs and inferred by another fuzzy logic controller system to obtain the objective quality of network video. Lastly, the performance of proposed method is validated on four videos with different network environment. Meanwhile this method is compared with other methods. The experimental results show that the proposed method can improve the similarity between subjective and objective assessment.

Error resilient concurrent video streaming over wireless mesh networks

In this paper, we propose a multi-source multi-path video streaming system for supporting high quality concurrent video-on-demand (VoD) services over wireless mesh networks (WMNs), and leverage forward error correction to enhance the error resilience of the system. By taking wireless interference into consideration, we present a more realistic networking model to capture the characteristics of WMNs and then design a route selection scheme using a joint rate/interference-distortion optimiza- tion framework to help the system optimally select concurrent streaming paths. We mathematically formulate such a route selec- tion problem, and solve it heuristically using genetic algorithm. Simulation results demonstrate the effectiveness of our proposed scheme.

In-Network Puncturing for Delay-Efficient Rate Control of Distributed Video Coding in Wireless Video Sensor Networks

Wyner-Ziv Video Coding （WZVC） is considered as a promising video coding scheme for Wireless Video Sensor Networks （WVSNs） due to its high compression efficiency and error resilience functionalities, as well as its low encoding complex- ity. To achieve a good Rate-Distortion （R-D） per- formance, the current WZVC paradi^prls usually a- dopt an end-to-end rate control scheme in which the decoder repeatedly requests the additional deco- ding data from the encoder for decoding Wyner-Ziv frames. Therefore, the waiting time of the additional decoding data is especially long in multihop WVSNs. In this paper, we propose a novel pro- gressive in-network rate control scheme for WZVC. The proposed in-network puncturing-based rate control scheme transfers the partial channel codes puncturing task from the encoder to the relay nodes. Then, the decoder can request the addition- al decoding data from the relay nodes instead of the encoder, and the total waiting time for deco- ding Wyner-Ziv frames is reduced consequently. Simulation results validate the proposed rate con- trol scheme.

Efficient Video Transmission in D2D Assisted Mobile Cloud Networks

The increasing popularity of smart mobile devices and the rise of online services has increased the requirements for efficient dissemination of social video contents. In this paper,we study the problem of distributing video from cloud server to users in partially connected cooperative D2 D network using network coding. In such a scenario, the transmission conflicts occur from simultaneous transmissions of multiple devices, and the scheduling decision should be made not only on the encoded packets but also on the set of transmitting devices. We analyze the lower bound and give an integer linear formulation of the joint optimization problem over the set of transmitting devices and the packet combinations.We also propose a heuristic solution for this setup using a conflict graph and local graph at every device. Simulation results show that our coding scheme significantly reduces the number of transmission slots, which will increase the efficiency of video delivery.

Video Based Fire Detection Systems on Forest and Wildland Using Convolutional Neural Network

The devastating effects of wildland fire are an unsolved problem,resulting in human losses and the destruction of natural and economic resources.Convolutional neural network(CNN)is shown to perform very well in the area of object classification.This network has the ability to perform feature extraction and classification within the same architecture.In this paper,we propose a CNN for identifying fire in videos.A deep domain based method for video fire detection is proposed to extract a powerful feature representation of fire.Testing on real video sequences,the proposed approach achieves better classification performance as some of relevant conventional video based fire detection methods and indicates that using CNN to detect fire in videos is efficient.To balance the efficiency and accuracy,the model is fine-tuned considering the nature of the target problem and fire data.Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in closed-circuit television surveillance systems compared to state-of-the-art methods.

Enabling QoE-aware Mobile Cloud Video Recording over Roadside Vehicular Networks

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

Joint routing and rate allocation for multiple video streams in ad-hoc wireless networks

The support for multiple video streams in an ad-hoc wireless network requires appropriate routing and rate allocation measures ascertaining the set of links for transmitting each stream and the encoding rate of the video to be delivered over the chosen links. The routing and rate allocation procedures impact the sustained quality of each video stream measured as the mean squared error (MSE) distortion at the receiver, and the overall network congestion in terms of queuing delay per link. We study the trade-off between these two competing objectives in a convex optimization formulation, and discuss both centralized and dis- tributed solutions for joint routing and rate allocation for multiple streams. For each stream, the optimal allocated rate strikes a balance between the selfish motive of minimizing video distortion and the global good of minimizing network congestions, while the routes are chosen over the least-congested links in the network. In addition to detailed analysis, network simulation results using ns-2 are presented for studying the optimal choice of parameters and to confirm the effectiveness of the proposed measures.

VBR WAVELET VIDEO CODING AND ITS RATE CONTROL IN ATM NETWORKS

This paper presents a new video coding system based on wavelet transform and its rate control scheme over ATM networks. First, three dimensional wavelet transform is performed for the original image sequence, and an extension of set partitioning in hierarchical trees algorithm is employed to quantize the wavelet coefficients. Then, the output rate of the coder is controlled at group of frame scale, ensuring that it conforms to the parameters of a leaky bucket controller. Several leaky buckets with different sizes are discussed too. Simulation shows the efficiency of this codec and the effectiveness of the proposed rate control scheme.

Next Generation Mobile Video Networking

HWANG Jenq-Neng received his Ph.D. degree from the University of Southern California, USA. In the summer of 1989, Dr. HWANG joined the De- partment of Electrical Engineering of the Universi- ty of Washington in Seattle, USA, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research fi＇om 2003 to 2005, and from 2011-2015. He is current- ly the Associate Chair for Global Affairs and Inter- national Development in the EE Depamnent. Hehas written more than 330 journal papers, conference papers and book chapters in the areas of machine learning, muhimedia signal processing, and muhimedia system integration and networking, including an au- thored textbook on ＂Multimedia Networking： from Theory to Practice,＂ published by Cambridge University Press. Dr. HWANG has close work- ing relationship with the industry on muhimedia signal processing and nmltimedia networking.

On the QoS Assessment of Video Sessions in Heterogeneous 3G-WLAN Networks with Seamless and Secure Mobility Support

Real-time video data transmission is currently emerging as a popular application among mobile users but it is very sensitive to QoS degradation due to packet losses in wireless networks.In order to achieve service continuity and integrity upon handoffs among heterogeneous networks,provisioning of seamless and secure mobility is required.However,in order to reduce the delay and packet losses during vertical handovers we need to employ supportive protocols like context transfer.In this paper we evaluate the QoS of video transmission over a heterogeneous 3G-WLAN network.The aggregate video data traffic is represented by a dynamic two-dimensional Markov chain model,which has been evaluated against real video data measurement.Upon the vertical handover, appropriate AAA handshaking and enhanced mobility management using context transfer have been considered.Perceived QoS evaluation of video streams was performed based on peak signal-noise ratio(PSNR) measurements,while we analyticallyestimated the number of packet losses during handovers.The results show that both packet loss within the converged network and loss occurrence affecting the perceived video quality is reduced. Moreover,the proposed context transfer scheme minimizes handover delay and the number of lost packet up to 3 times compared to standard AAA handshaking.

Evolution Handoff Strategy for Real-Time Video Transmission over Practical Cognitive Radio Networks

The transmission delay of realtime video packet mainly depends on the sensing time delay(short-term factor) and the entire frame transmission delay(long-term factor).Therefore,the optimization problem in the spectrum handoff process should be formulated as the combination of microscopic optimization and macroscopic optimization.In this paper,we focus on the issue of combining these two optimization models,and propose a novel Evolution Spectrum Handoff(ESH)strategy to minimize the expected transmission delay of real-time video packet.In the microoptimized model,considering the tradeoff between Primary User's(PU's) allowable collision percentage of each channel and transmission delay of video packet,we propose a mixed integer non-linear programming scheme.The scheme is able to achieve the minimum sensing time which is termed as an optimal stopping time.In the macro-optimized model,using the optimal stopping time as reward function within the partially observable Markov decision process framework,the EHS strategy is designed to search an optimal target channel set and minimize the expected delay of packet in the long-term real-time video transmission.Meanwhile,the minimum expected transmission delay is obtained under practical cognitive radio networks' conditions,i.e.,secondary user's mobility,PU's random access,imperfect sensing information,etc..Theoretical analysis and simulation results show that the ESH strategy can effectively reduce the transmission delay of video packet in spectrum handoff process.

Evaluate mobile video quality with LTE radio access network parameters

To evaluate the video quality, we tested sample videos delivered using HTTP adaptive streaming （HAS） in LTE network. In order to establish a correlation between radio access network （RAN） performance and quality of experience （ QoE）, we set up a testbed under different radio im- pairment conditions with three parameters： signal to interference and noise ratio （ SINR）, an amount of available network resource and a round trip latency. End users graded each video in a mobile equipment with their QoE Mearnwhile, we used a nonlinear model to simulate the comprehensive pre- dicted mean opinion score （pMOS）. Our results show that the nonlinear model can predict the enduser＇ s feedback. The pearson correlation coefficient （PCC） of the model is larger than 0. 9. This demonstrate that the output of the model has a high correlation with the end users＇ ratings and can reflect the QoE accurately. The method we developed will help mobile network operators evaluate the RAN performance of its QoE. It can also be used for HAS service to optimize LTE network and improve its QoE.

AVariablebitrateVideoCodingSchemeBasedonMorphologicalOperationsforATMNetworks

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Study on an Audio and Video Network Monitoring System for Weather Modification Operation

An audio and video network monitoring system for weather modification operation transmitting information by 3G, ADSL and Internet has been developed and applied in weather modification operation of Tai＇an City. The all-in-one machine of 3G audio and video network highly integrates all front-end devices used for audio and video collection, communication, power supply and information storage, and has advantages of wireless video transmission, clear two-way voice intercom with the command center, waterproof and dustproof function, simple operation, good portability, and long working hours. Compression code of the system is transmitted by dynamic bandwidth, and compression rate varies from 32 kbps to 4 Mbps under different network conditions. This system has forwarding mode, that is, monitoring information from each front-end monitoring point is trans- mitted to the server of the command center by 3G/ADSL, and the server codes＇and decodes again, then beck-end users call images from the serv- er, which can address 3G network stoppage caused by many users calling front-end video at the same time. In addition, the system has been ap- plied in surface weather modification operation of Tai＇an City, and has made a great contribution to transmitting operation orders in real time, monitoring, standardizing and recording operating process, and improving operating safety.

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.

商业模式创新对网络视频平台用户价值共创的作用机理

数字经济发展需要互联网技术和数字技术作支撑,引导消费者网络视频消费沿着“简单用户搜索—网络平台展示推荐—网络平台算法推荐”的路径演变,这对企业的商业模式创新提出了挑战。本文基于企业内外部价值网络理论,以“平台质量-用户认知-用户行为”的逻辑分析思路,构建商业模式创新对网络视频平台价值共创的作用机制模型并检验其作用机理。结果显示:商业模式创新的内容质量创新、交互质量变革及创作激励创新对用户认知有促进作用,网络视频平台用户认知对价值共创有促进作用。因此建议:以网络视频平台需求为依据,选择相匹配的商业模式创新方案;加大投入力度,鼓励网络平台关联企业创作视频内容;构建有效链接纽带,畅通平台企业与用户的沟通渠道。

Joint Content Placement and Traffic Management for Cloud Mobile Video Distribution over Software-Defined Networks

To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking(SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

多层级视频会议系统跨网段融合技术的应用

通过介绍天津市地震局应急视频会议系统接入中国地震局视频会议系统、天津市政府视频系统和天津应急管理局视频系统的基本情况,结合地震应急视频会议系统现状,分析多类型、多层级、多网段视频会议系统的架构特点,着重介绍了多网段、多视频源视频转发优化技术在视频会议系统融合中的应用。通过该技术的应用,实现了天津市地震应急视频会议系统与各相关单位视频会议系统的全部连通。

Action Recognition in Surveillance Videos with Combined Deep Network Models

Action recognition is an important topic in computer vision. Recently, deep learning technologies have been successfully used in lots of applications including video data for sloving recognition problems. However, most existing deep learning based recognition frameworks are not optimized for action in the surveillance videos. In this paper, we propose a novel method to deal with the recognition of different types of actions in outdoor surveillance videos. The proposed method first introduces motion compensation to improve the detection of human target. Then, it uses three different types of deep models with single and sequenced images as inputs for the recognition of different types of actions. Finally, predictions from different models are fused with a linear model. Experimental results show that the proposed method works well on the real surveillance videos.

基于UNet3+生成对抗网络的视频异常检测

为解决传统视频异常检测方法在不同场景下多尺度特征提取不完全的问题,提出两种方法:一种是用于简单场景的基于UNet3+的生成对抗网络方法(简称U3P^(2)),另一种是用于复杂场景的基于UNet++的生成对抗网络方法(简称UP^(3))。两种方法分别对连续输入的视频帧生成预测,引入多种损失函数和光流模型学习其外观与运动信息,通过计算AUC进行性能评估。U3P^(2)方法以6.3 M参数量在Ped2数据集的AUC提升约0.6%,而UP^(3)方法在Avenue数据集的AUC提升约0.8%,验证其能够有效应对不同场景下的异常检测任务。