IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i3d10.1007_s13198-021-01220-w.html
   My bibliography  Save this article

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

Author

Listed:
  • Jiadi Yang

    (Hebei Academy of Fine Arts)

  • Jinjin Wang

    (Hebei Academy of Fine Arts)

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.

Suggested Citation

  • Jiadi Yang & Jinjin Wang, 2022. "TV program innovation and teaching under big data background in all media era," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1031-1041, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01220-w
    DOI: 10.1007/s13198-021-01220-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01220-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01220-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ye, Lisha & Pan, Shan L & Wang, Jingyuan & Wu, Junjie & Dong, Xiaoying, 2021. "Big data analytics for sustainable cities: An information triangulation study of hazardous materials transportation," Journal of Business Research, Elsevier, vol. 128(C), pages 381-390.
    2. Amin Khalil Alsadi & Thamir Hamad Alaskar & Karim Mezghani, 2021. "Adoption of Big Data Analytics in Supply Chain Management: Combining Organizational Factors With Supply Chain Connectivity," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 14(2), pages 88-107, April.
    3. Abdellah Chehri & Issouf Fofana & Xiaomin Yang, 2021. "Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    4. Haiying Wang & Yanyuan Ma, 2021. "Optimal subsampling for quantile regression in big data," Biometrika, Biometrika Trust, vol. 108(1), pages 99-112.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ziyang Wang & HaiYing Wang & Nalini Ravishanker, 2023. "Subsampling in Longitudinal Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-29, March.
    2. Deng, Jiayi & Huang, Danyang & Ding, Yi & Zhu, Yingqiu & Jing, Bingyi & Zhang, Bo, 2024. "Subsampling spectral clustering for stochastic block models in large-scale networks," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    3. Philippe Funk, 2022. "Artificial Intelligence And Cybersecurity Implications For Business Management," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 16(1), pages 252-261.
    4. Seddigh, Mohammad Reza & Targholizadeh, Aida & Shokouhyar, Sajjad & Shokoohyar, Sina, 2023. "Social media and expert analysis cast light on the mechanisms of underlying problems in pharmaceutical supply chain: An exploratory approach," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    5. Baolin Chen & Shanshan Song & Yong Zhou, 2024. "Estimation and testing of expectile regression with efficient subsampling for massive data," Statistical Papers, Springer, vol. 65(9), pages 5593-5613, December.
    6. Yujing Shao & Lei Wang, 2022. "Optimal subsampling for composite quantile regression model in massive data," Statistical Papers, Springer, vol. 63(4), pages 1139-1161, August.
    7. Daouia, Abdelaati & Padoan, Simone A. & Stupfler, Gilles, 2022. "Optimal weighted pooling for inference about the tail index and extreme quantiles," TSE Working Papers 22-1322, Toulouse School of Economics (TSE), revised 07 Jun 2023.
    8. Xiaohui Yuan & Yong Li & Xiaogang Dong & Tianqing Liu, 2022. "Optimal subsampling for composite quantile regression in big data," Statistical Papers, Springer, vol. 63(5), pages 1649-1676, October.
    9. Anna Kwiotkowska & Bożena Gajdzik & Radosław Wolniak & Jolita Vveinhardt & Magdalena Gębczyńska, 2021. "Leadership Competencies in Making Industry 4.0 Effective: The Case of Polish Heat and Power Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    10. Aparna Kumari & Rushil Kaushikkumar Patel & Urvi Chintukumar Sukharamwala & Sudeep Tanwar & Maria Simona Raboaca & Aldosary Saad & Amr Tolba, 2022. "AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    11. Huy N. Chau & J. Lars Kirkby & Dang H. Nguyen & Duy Nguyen & Nhu N. Nguyen & Thai Nguyen, 2024. "On the Inversion‐Free Newton's Method and Its Applications," International Statistical Review, International Statistical Institute, vol. 92(2), pages 284-321, August.
    12. Mehrdad Aslani & Hamed Hashemi-Dezaki & Abbas Ketabi, 2021. "Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios," Sustainability, MDPI, vol. 13(10), pages 1-30, May.
    13. Jun Yu & Jiaqi Liu & HaiYing Wang, 2023. "Information-based optimal subdata selection for non-linear models," Statistical Papers, Springer, vol. 64(4), pages 1069-1093, August.
    14. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    15. Qiang Wang & Dong Yu & Jinyu Zhou & Chaowu Jin, 2023. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    16. Ayman wael AL-Khatib & Ahmed Shuhaiber, 2022. "Green Intellectual Capital and Green Supply Chain Performance: Does Big Data Analytics Capabilities Matter?," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    17. Tomáš Loveček & Lenka Straková & Katarína Kampová, 2021. "Modeling and Simulation as Tools to Increase the Protection of Critical Infrastructure and the Sustainability of the Provision of Essential Needs of Citizens," Sustainability, MDPI, vol. 13(11), pages 1-18, May.
    18. Tianzhen Wang & Haixiang Zhang, 2022. "Optimal subsampling for multiplicative regression with massive data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(4), pages 418-449, November.
    19. Wang, Yonggui & Tian, Qinghong & Li, Xia & Xiao, Xiaohong, 2022. "Different roles, different strokes: How to leverage two types of digital platform capabilities to fuel service innovation," Journal of Business Research, Elsevier, vol. 144(C), pages 1121-1128.
    20. Ferdaus, Md Meftahul & Dam, Tanmoy & Anavatti, Sreenatha & Das, Sarobi, 2024. "Digital technologies for a net-zero energy future: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01220-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.