Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
Video quality assurance in multi-source streaming techniques
Proceedings of the 4th international IFIP/ACM Latin American conference on Networking
Automatic quality of experience measuring on video delivering networks
ACM SIGMETRICS Performance Evaluation Review
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
Classification of slice-based VBR video traffic and estimation of link loss by exceedance
Computer Networks: The International Journal of Computer and Telecommunications Networking
An autonomic architecture for optimizing QoE in multimedia access networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Evaluation of gatekeeper proxies for firewall traversal in secure videoconferencing systems
International Journal of Internet Protocol Technology
A versatile model for packet loss visibility and its application to packet prioritization
IEEE Transactions on Image Processing
QoE monitoring platform for video delivery networks
IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
Graceful degradation in 3GPP MBMS mobile TV services using H.264/AVC temporal scalability
EURASIP Journal on Wireless Communications and Networking
Evaluating quality of experience for streaming video in real time
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
On the use of random neural networks for traffic matrix estimation in large-scale IP networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
No-reference quality of experience monitoring in DVB-H networks
WTS'10 Proceedings of the 9th conference on Wireless telecommunications symposium
A study on QoS of VoIP networks: a random neural network (RNN) approach
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
An initiative for a classified bibliography on G-networks
Performance Evaluation
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Radio resource management in emerging heterogeneous wireless networks
Computer Communications
WWIC'11 Proceedings of the 9th IFIP TC 6 international conference on Wired/wireless internet communications
User-perceived quality assessment of streaming media using reduced feature sets
ACM Transactions on Internet Technology (TOIT)
Evaluating users’ satisfaction in packet networks using random neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Designing a collector overlay architecture for fault diagnosis in video networks
Computer Communications
Video quality estimator for wireless mesh networks
Proceedings of the 2012 IEEE 20th International Workshop on Quality of Service
QoE-oriented 3D video transcoding for mobile streaming
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section of best papers of ACM multimedia 2011, and special section on 3D mobile multimedia
A network management algorithm and protocol for improving QoE in mobile IPTV
Computer Communications
Neuro-Fuzzy approach to video transmission over ZigBee
Neurocomputing
Optimum piece selection strategies for a peer-to-peer video streaming platform
Computers and Operations Research
Metrics and QoE assessment in P2PTV applications
International Journal of Internet Protocol Technology
| Hi-index | 0.00 |
An important and unsolved problem today is that of automatic quantification of the quality of video flows transmitted over packet networks. In particular, the ability to perform this task in real time (typically for streams sent themselves in real time) is especially interesting. The problem is still unsolved because there are many parameters affecting video quality, and their combined effect is not well identified and understood. Among these parameters, we have the source bit rate, the encoded frame type, the frame rate at the source, the packet loss rate in the network, etc. Only subjective evaluations give good results but, by definition, they are not automatic. We have previously explored the possibility of using artificial neural networks (NNs) to automatically quantify the quality of video flows and we showed that they can give results well correlated with human perception. In this paper, our goal is twofold. First, we report on a significant enhancement of our method by means of a new neural approach, the random NN model, and its learning algorithm, both of which offer better performances for our application. Second, we follow our approach to study and analyze the behavior of video quality for wide range variations of a set of selected parameters. This may help in developing control mechanisms in order to deliver the best possible video quality given the current network situation, and in better understanding of QoS aspects in multimedia engineering.