Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
As a popular form of virtual reality (VR) media, omnidirectional video (OV) has been continuously developed in recent years. OV\ncontains the view of the scene in every direction, which will ask for around 120 Mbps with 8k resolution and 25 fps (frames per\nsecond). Although there has been a lot of work to optimize the transmission for on-demand of OV, the research on the live\nstreaming of OV is still very lacking. Another big challenge for the OV live streaming system is the huge demand for computing\nresources. ,The existing terminal devices are difficult to completely carry tasks such as stitching, encoding, and rendering. ,is\npaper proposes a mobile edge assisted live streaming system for omnidirectional video (MELiveOV); the MELiveOV can intelligently\noffload the processing tasks to the edge computing enabled 5G base stations. ,The MELiveOV consists of an omnidirectional\nvideo generation module, a streaming module, and a viewpoint prediction module. A prototype system of MELiveOV\nis implemented to prove its complete end-to-end OV live streaming service. Evaluation result demonstrates that compared with\nthe traditional solution, MELiveOV can reduce the network bandwidth requirement by about 50% and the transmission delay of\nmore than 70% while ensuring the quality of the userâ??s experience....
Intelligent analysis of surveillance videos over networks requires high recognition accuracy\nby analyzing good-quality videos that however introduce significant bandwidth requirement.\nDegraded video quality because of high object dynamics under wireless video transmission induces\nmore critical issues to the success of smart video surveillance. In this paper, an object-based source\ncoding method is proposed to preserve constant quality of video streaming over wireless networks.\nThe inverse relationship between video quality and object dynamics (i.e., decreasing video quality\ndue to the occurrence of large and fast-moving objects) is characterized statistically as a linear model.\nA regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model\nwith respect to different bitrates. The linear model is applied to predict the bitrate increment required\nto enhance video quality. A simulated wireless environment is set up to verify the proposed method\nunder different wireless situations. Experiments with real surveillance videos of a variety of object\ndynamics are conducted to evaluate the performance of the method. Experimental results demonstrate\nsignificant improvement of streaming videos relative to both visual and quantitative aspects....
As the demand for over-the-top and online streaming services exponentially increases,\nmany techniques for Quality of Experience (QoE) provisioning have been studied. Users can take\nactions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern\nof users rather than the network condition or video quality. In this context, we propose a proactive\ncontent-loading algorithm for improving per-user personalized preferences using multinomial\nsoftmax classification. Based on experimental results, the proposed algorithm has a personalized\nper-user content waiting time that is significantly lower than that of competing algorithms....
It is a difficult task to estimate the human transition motion without the specialized software. The 3-dimensional (3D) human\nmotion animation is widely used in video game, movie, and so on. When making the animation, human transition motion is\nnecessary. If there is a method that can generate the transition motion, the making time will cost less and the working efficiency\nwill be improved.Thus a new method called latent space optimization based on projection analysis (LSOPA) is proposed to estimate\nthe human transition motion. LSOPA is carried out under the assistance of Gaussian process dynamical models (GPDM); it builds\nthe object function to optimize the data in the low dimensional (LD) space, and the optimized data in LD space will be obtained\nto generate the human transition motion.The LSOPA can make the GPDM learn the high dimensional (HD) data to estimate the\nneeded transition motion.The excellent performance of LSOPA will be tested by the experiments....
With more and more new mobile devices (such as mobile phones, tablets, and wearable devices) entering peopleâ??s daily life, along\nwith the application and development of relevant technologies based on usersâ?? location information, location based service is\nbecoming a basic service demand of peopleâ??s life. This paper puts forward a research on location technology based on frequency\nmodulation band digital audio broadcasting (FMChina Digital Radio, FM-CDR).A newmethod of adding timestamp information\nto the FM-CDR frame structure is proposed, which verified that the change to the system does not affect the normal transmission\nand reception of broadcast content under the original standards and can accurately extract the recognition signal and timing\ninformation of BS. In the complex environment, the estimation algorithm of signal parameters such as received signal strength\n(RSS), time of arrival (TOA), and time difference of arrival (TDOA) of terrestrial radio broadcast signals is studied. In this paper, a\nnew method based on multisource data fusion is proposed, which can meet the need of localization in various environments and\novercome the deficiency of single localization method....
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