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Prediction Methods for MPEG-4 and H.264 Video Transmission

Filip Pilka, Miloš Oravec
2011 Journal of Electrical Engineering  
Therefore we illustrate the results of neural networks for video traffic prediction using both mpeg-4 and h.264 trace files.  ...  Therefore understanding of video coding standards and video traffic sources, such as video trace files is highly important.  ...  NEURAL NETWORKS In this paper the neural networks are used as a tool for video traffic prediction.  ... 
doi:10.2478/v10187-011-0010-6 fatcat:kekkvhy4yzdupczcxaci5ehm6m

Adaptive Rate Control Low Bit-Rate Video Transmission over Wireless Zigbee Networks

A. Zainaldin, I. Lambadaris, B. Nandy
2008 2008 IEEE International Conference on Communications  
The proposed schemes enable to transmit video over ZigBee with minimum data loss and excellent picture quality.  ...  The first Neuro-Fuzzy scheme take care that buffer neither oversupplied nor starved with video data. The second Neuro-Fuzzy scheme ensures the departure rate meets the traffic condition of ZigBee.  ...  MPEG-4 ENCODR Using video compression we can transmit or manipulate video data very easy and fast. Video compression maximizes reconstruction quality and minimizes video file size.  ... 
doi:10.1109/icc.2008.18 dblp:conf/icc/ZainaldinLN08 fatcat:jf6n2idyifbynkx6w2yg7q2yim

Guest Editorial Introduction to Special Section on Learning-Based Image and Video Compression

Shan Liu, Wen-Hsiao Peng, Lu Yu
2020 IEEE transactions on circuits and systems for video technology (Print)  
It is estimated that in 2020, 82% of global IP traffic and 79% of global Internet traffic will come from video; globally 3 trillion minutes (5 million years) of video content will cross the Internet each  ...  The rapidly increasing consumption of storage capacity and transmission bandwidth from video, especially HD and UHD video content, has made video compression a critical stage to guarantee the quality of  ... 
doi:10.1109/tcsvt.2020.2995955 fatcat:qv3h5hpjq5gu7l324mbzwjjjmm

Editorial Applied Artificial Intelligence and Machine Learning for Video Coding and Streaming

Marta Mrak Marta, Mahmoud Hashemi, Shervin Shirmohammadi, Ying Chen, Moncef Gabbouj
2021 IEEE Open Journal of Signal Processing  
and constituted 71% of all 2020 IP traffic. 1 Therefore, improving video coding methods and video networking schemes is vital to cope with this increasing demand.  ...  Video continues to be the dominant traffic on the Internet, and especially due to the COVID-19 pandemic causing increased video usage, video traffic in the USA increased by 70% in 2020 compared to 2019  ...  Therefore, this paper contributes to both the video quality enhancement and video compression pipelines.  ... 
doi:10.1109/ojsp.2021.3105305 fatcat:ayzqsqfohvfevafs2cc2ozrxjy

Machine Learning for Multimedia Communications

Nikolaos Thomos, Thomas Maugey, Laura Toni
2022 Sensors  
For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved.  ...  In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.  ...  A deep neural network is used to extract the areas of interest from a low-quality video.  ... 
doi:10.3390/s22030819 pmid:35161566 pmcid:PMC8840624 fatcat:nmz7s6ei3bdddarbt2bqhf6beu

Efficient Video Compression via Content-Adaptive Super-Resolution [article]

Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh
2021 arXiv   pre-print
This paper presents a new approach that augments existing codecs with a small, content-adaptive super-resolution model that significantly boosts video quality.  ...  Video compression is a critical component of Internet video delivery.  ...  Our primary finding is that a SR neural network adapted in this manner over the course of a video can provide such a boost to quality, that including a model stream along with the compressed video is more  ... 
arXiv:2104.02322v1 fatcat:stvwlttugrhglpnpbskz2g2ile

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources

A.D. Doulamis, N.D. Doulamis, S.D. Kollias
2003 IEEE Transactions on Neural Networks  
The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases.  ...  In this paper, an adaptable neural-network architecture is proposed covering both cases.  ...  that achieve an acceptable video quality.  ... 
doi:10.1109/tnn.2002.806645 pmid:18237998 fatcat:x7lwm4pxbvgl7h3cyobxnefgzm

Optimal nonlinear adaptive prediction and modeling of MPEG video in ATM networks using pipelined recurrent neural networks

Po-Rong Chang, Jen-Tsung Hu
1997 IEEE Journal on Selected Areas in Communications  
This paper investigates the application of a pipelined recurrent neural network (PRNN) to the adaptive traffic prediction of MPEG video signal via dynamic ATM networks.  ...  The PRNN-based predictor presented in this paper is shown to be promising and practically feasible in obtaining the best adaptive prediction of real-time MPEG video traffic.  ...  To overcome this difficulty, an alternative architecture to the traffic prediction of MPEG video with the flexibility to adapt to a changing ATM network environment is based on recurrent neural networks  ... 
doi:10.1109/49.611161 fatcat:agunurzkyfhz3ehomo6y2kotxq

A dynamic bandwidth resource allocation based on neural networks in euroskyway multimedia satellite system

P. Camarda, M. Castellano, G. Piscitelli, D. Striccoli, G. Tomasicchio
2003 International Journal of Communication Systems  
The approach perform an online estimation of expected resource requests implementing traffic resource assignment by using a sub-symbolic adaptive representation of the traffic source.  ...  An accurate design of neural network architecture and the low-cost industrial availability of this novel technology lead to an optimal trade-off between customer satisfaction in terms of QoS and system  ...  Traffic Source The source data utilized for our analysis consist of portions of video streams codified with the MPEG-1 video standard compression [12] .  ... 
doi:10.1002/dac.578 fatcat:sngepiw6vbagrmyb5ccyskpi2y

Wireless Sensor Networks to Improve Road Monitoring [chapter]

M. Collotta, G. Pau, V.M. Salerno, G. Scat
2012 Wireless Sensor Networks - Technology and Applications  
Image compression Vs. video compression Compression standards use different methods and have various transmission rate, quality and latency.  ...  With effective compression techniques, it is possible to obtain a considerable file size reduction with minimal effects on image quality.  ... 
doi:10.5772/48505 fatcat:rllp24pxhfairl5o5ohl3l2ewu

QoE-aware Video Rate Adaptation algorithms in multi-user IEEE 802.11 wireless networks

Federico Chiariotti, Chiara Pielli, Andrea Zanella, Michele Zorzi
2015 2015 IEEE International Conference on Communications (ICC)  
video quality as network conditions change.  ...  The spreading of video streaming services in the last few years is presenting new challenges in wireless networking; Video Rate Adaptation (VRA) is a technique that optimizes the bandwidth usage by adapting  ...  ACKNOWLEDGMENT This work was supported by the project A Novel Approach to Wireless Networking based on Cognitive Science and Distributed Intelligence, funded by Fondazione CaRiPaRo under the framework  ... 
doi:10.1109/icc.2015.7249297 dblp:conf/icc/ChiariottiPZZ15 fatcat:ox2tgmu3sndmrg3yefbipz34vm

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
Liu, H., +, TCSVT Feb. 2019 486-501 Traffic engineering computing Adaptive Deep Convolutional Neural Networks for Scene-Specific Object Detection.  ...  ., +, TCSVT May 2019 1408-1422 Blind Video Quality Assessment With Weakly Supervised Learning and Res- ampling Strategy.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions [article]

Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
2020 arXiv   pre-print
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360-degree videos to video-conferencing and live streaming.  ...  In this paper, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality.  ...  Hence, additional techniques, such as adaptive bitrate and neural enhancement, have been introduced that enable the dynamic adaptation to the varying quality of the channel. Adaptive Bitrate.  ... 
arXiv:2010.05838v2 fatcat:bgzcbhewpbge3e7idxpvewa4di

Doctoral Consortium - Paper 3 - Wilmer Moina-Rivera [article]

Wlimer Moina-Rivera
2020 Figshare  
Title: Video Encoding Cloud System for High Performance ScenariosAuthor: Wilmer Moina-Rivera  ...  The proposed system split the video in scenes and encodes each scene by selecting the coding parameters adaptively in order to achieve a target quality value.  ...  However, determining the coding parameters so that each segment of video processed in a distributed environment has the highest quality, the ability to adapt to the bandwidth of clients and the capabilities  ... 
doi:10.6084/m9.figshare.12479348.v1 fatcat:2bk6e767b5eqtl22pno53nij6u

Fuzzy Logic Controller for Wireless Video Transmission

2011 Journal of Computer Science  
The traffic shaping buffer is used to prevent excess back-to-back transmission of video signals.  ...  Results: Simulation results showed that the use of intelligent fuzzy logic and nero-fuzzy controller improved the data transmission rate and decreased long delay when compared with other conventional methods  ...  The traffic-shaper releases two sources of data. The MPEG encoder compressed video sequence and the data kept in the traffic-shaper.  ... 
doi:10.3844/jcssp.2011.1119.1127 fatcat:vwide4jfc5avjjrmcjyqy5uhq4
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