Jiang, W., Fu, Z., Peng, J., et al.: 4 Bit-based gradient compression method for distributed deep learning system. Wang, T., He, X., Sun, W., et al.: Improved HEVC intra coding compression algorithm combined with convolutional neural network. Gu, H.: Data compression coding technologies for computer-generated holographic three-dimensional display. Pan, P., Yao, Y., Wang, H.: Detection of double compression for HEVC videos with the same coding parameters. Wang, G., Jin, Y., Peng, H., et al.: Error correction of Lempel-Ziv-Welch compressed data.

Wang, H., Ma, J., Qu, J.: Design and implementation of full HD video real-time compression coding and storage system. Wu, Y., Peng, Y., Lu, A.: Research on image compression coding technology based on deep learning CNN. Guo, J., Cao, L., Zhu, F.: Progressive compression method of real-time data under load balancing strategy. The experimental results show that the method has good coding performance for test sequences with different contents and different resolutions. CNN encoder uses a series of down-sampling convolution and ReLU nonlinear mapping to extract and fuse global information, and conducts analysis on areas with low human visual sensitivity on the HEVC transform domain. Finally, an intra-prediction algorithm for video data is established based on CNN. Then, the coding structure adjustment parameters are calculated, and after coding, the determined reference frame and the bit consumption caused by using the reference frame can be obtained. First, the multimedia video coding unit is divided, and the coded frames are arranged in POC order for coding. To solve this problem, this study proposes an efficient compression coding method for multimedia video data based on CNN. Due to the phenomenon of object occlusion and inconsistent motion of different objects in video, high-efficiency compression and coding of video data will generate prediction residuals related to texture structure.

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