Real-time big data analytics for multimedia transmission and storage
Kun Wang, Jun Mi, Chenhan Xu, Lei Shu, Der‐Jiunn Deng · 2016
With increase demand on wireless services, equipment supporting multimedia applications has been becoming more and more popular in recent years. With billions of devices involved in mobile Internet, data volume is undergoing an extremely rapid growth. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive study has been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this paper, a hybrid-stream big data analytics model is proposed to perform big data video analysis. This model contains four procedures, i.e., data preprocessing, data classification, data recognition and data load reduction. Specifically, an innovative multi-dimensional Convolution Neural Network (CNN) is proposed to obtain the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm. Then, a reliable key frame extraction mechanism will recognize the importance of each frame or clip and then decide whether to abandon it automatically by a series of correlation operations. Simulation results illustrate that the size of the processed video has been effectively reduced. The simulation also shows that proposed model performs steadily and is robust enough to keep up with the big data crush in multimedia era.