Abstract
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. To solve this problem, this study proposes an efficient compression coding method for multimedia video data based on CNN. First, the multimedia video coding unit is divided, and the coded frames are arranged in POC order for coding. 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. Finally, an intra-prediction algorithm for video data is established based on CNN. 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. Frequency coefficient suppression. The experimental results show that the method has good coding performance for test sequences with different contents and different resolutions.
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Funding
Higher Education Teaching Reform Research Project of Jilin Province: Construction and Practice of “Online and Offline” Hybrid Teaching Mode for Film and Television Arts Majors in Universities under MOOC Environment (20213F2ENY4001J).
Education Science “Fourteen Five-Year” Project of Jilin Province: Research on the Construction of College of Modern Industry for Film and Television Media Major (ZD21088).
China Association of Private Education 2022 Annual Planning Project (School Development): Research on the Construction of College of Modern Industry for Film and Television Media Major (CANFZG22274).
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, X., Wu, Y. (2023). An Efficient Compression Coding Method for Multimedia Video Data Based on CNN. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-28787-9_10
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DOI: https://doi.org/10.1007/978-3-031-28787-9_10
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