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TV program innovation and teaching under big data background in all media era

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Abstract

The purpose is to study how to innovate and teach TV programs in the background of big data. Shot boundary detection technology is adopted to search the content of TV programs video. The content retrieval of TV program is realized by shot boundary detection technology, which mainly includes two aspects of decompressed domain and compressed domain. Regarding the decompressed domain, a new abrupt shot change detection algorithm for decompressed domain is adopted to analyze of the whole search process of decompressed domain shot boundary. Regarding the compressed domain, the algorithm of video shot boundary detection on H.264/AVC code stream is used. Experimental results show that shot detection algorithm can detect not only abrupt shot change, but also gradual change. In the experiment, the comprehensive detection performance of various frequency sequences achieves 94% recall and 93.2% accuracy. The recall rate of abrupt shot change detection algorithm for experimental data is 94.5%, and the accuracy rate is 97.6%, which is superior to the detection performance of existing abrupt shot detection methods, and has a certain application value. Meanwhile, the similar video fast retrieval algorithm, the MinHash algorithm and LSH (Locality Sensitive Hashing) algorithm are compared. Similar video fast retrieval algorithm can achieve fast clustering of similar video faster, and can effectively retrieve similar video, so as to complete the fast retrieval of large-scale video data. The use of new abrupt shot change detection algorithm for decompressed domain and shot boundary detection algorithm in TV programs, to a large extent, optimizes the management of TV advertising and the manual broadcast of TV programs; moreover, it saves manpower and the broadcast cost of TV programs, which is a reform and innovation of traditional TV programs. In the future research, the boundary detection technology can be optimized to better play high-quality TV pictures.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

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This research received no external funding.

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Authors and Affiliations

  1. Academy of Film and Television Arts, Hebei Academy of Fine Arts, Shijiazhuang, 050700, China

    Jiadi Yang

  2. Academy of Architecture and Art Design, Hebei Academy of Fine Arts, Shijiazhuang, 050700, China

    Jinjin Wang

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  1. Jiadi Yang
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  2. Jinjin Wang
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Correspondence to Jiadi Yang.

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Yang, J., Wang, J. TV program innovation and teaching under big data background in all media era. Int J Syst Assur Eng Manag 13 (Suppl 3), 1031–1041 (2022). https://doi.org/10.1007/s13198-021-01220-w

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