Multimedia Big Data Frame Combination Storage Strategy Based on Virtual Space Distortion

Abstract

Aiming at the transmission reliability and storage management of multimedia data, the multimedia big data frame combination storage strategy is proposed based on the virtual space distortion analysis model. On the one hand, the virtual space distortion analysis model is established. This model can be used to find the high similarity of video frames and flow structures in different multimedia streams. The model can be used to uniform the multimedia stream state and effectively improve the efficiency of data mining. The model can effectively analyze the difference between the multimedia stream of virtual space and the storage of the server group. On the other hand, based on the distorted efforts of the virtual space and heterogeneous of the multimedia data stream, combined with big data multimedia service requirements, the big data frame combination of storage space and virtual space integration would be integrated as a whole through the multimedia data frame combination and distorted perception. Finally, the combination of multimedia data frames is proposed. The delay, channel, storage and big data processing level of the virtual space storage scheme and were analyzed and evaluated by the simulation experiment, compared with the physical storage scheme of the multimedia big data. The experimental results show that the proposed scheme has significant advantages in the above aspects.