Real-Time Load Reduction in Multimedia Big Data for Mobile Internet

Abstract

In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than ever before, and Internet traffic is rapidly increasing, not only in volume but also in heterogeneity. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive papers have been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this article, a hybrid-stream model is proposed to solve these problems for video analysis. Functionality of this model covers Data Preprocessing, Data Classification, and Data-Load-Reduction Processing. Specifically, an improved Convolutional Neural Networks (CNN) classification algorithm is designed to evaluate the importance of each video frame and video clip to enhance classification precision. Then, a reliable keyframe extraction mechanism will recognize the importance of each frame or clip, and decide whether to abandon it automatically by a series of correlation operations. The model will reduce data load to a dynamic threshold changed by σ, control the input size of the video in mobile Internet, and thus reduce network overload. Through experimental simulations, we find that the size of processed video has been effectively reduced and the quality of experience (QoE) has not been lowered due to a suitably selected parameter η. The simulation also shows that the model has a steady performance and is powerful enough for continuously growing multimedia big data.

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