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Culture Information Technology (CIT)

A Study on the Application of AI-Based Video Editing in Broadcasting : Focusing on TV Programs

초록

영어

This study aims to examine how Artificial Intelligence (AI) can be practically applied to post-production in broadcasting and to evaluate its implications for future content creation. To this end, it focuses on the case of Earth Sweepers, a 2024 MBC reality variety program recognized as the first terrestrial TV show in Korea to adopt AI-based editing and de-identification under outdoor filming conditions. The research method involved analyzing the integration of two key technologies: automatic editing developed by the Electronics and Telecommunications Research Institute (ETRI) and de-identification technology developed by the Korea Electronics Technology Institute (KETI). Both tools were implemented as plug-ins for Adobe Premiere Pro to ensure seamless use within established editing workflows. The results revealed that AI significantly reduced production time—overall editing was shortened by approximately 35–40%, and de-identification achieved over 90% accuracy while cutting costs by more than 20 million KRW per episode. AI-assisted editing also improved multicamera alignment, scene segmentation, and highlight extraction, while de-identification enhanced both visual quality and compliance with regulatory requirements. The findings highlight the potential of human-AI collaboration in broadcasting and underscore the importance of establishing standardization, professional training, and institutional support to ensure sustainable adoption. This research provides valuable insights for guiding innovation and shaping the future ecosystem of AI broadcasting.

목차

Abstract
1. Introduction
2. Background Theory
2.1 AI-Based Post-Production
3. Case Studies of AI Technology in Broadcast Production
3.1 AI Auto Editing Technology
3.2 AI-Based Compositing Technology
4. Experimental Results and Discussion: MBC’s “Earth Sweepers”
4.1 Experiment Overview
4.2 Results and Discussion
4. Discussion
5. Conclusion
Acknowledgement
References

저자정보

  • Kyunghee KIM Ph.D. Candidate, Department of Immersive Convergence Content, Kwangwoon University
  • Jaehyun LEE Postdoctoral researcher, Department of Immersive Convergence Content, Kwangwoon University
  • Soonchul KWON Associate Professor, Department of Immersive Convergence Content, Kwangwoon University
  • Seunghyun LEE Professor, Department of Immersive Convergence Content, Kwangwoon University

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