• Title/Summary/Keyword: 스크린 쿼터

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Macro Analysis on the Supply and Demand of New-coming Directors in the Korean Movie Industry over the Years (1971-2016) (신인 영화감독의 한국영화시장 진출에 대한 거시 분석)

  • Kim, Jung-Ho;Kim, Jae Sung
    • The Journal of the Korea Contents Association
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    • v.17 no.4
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    • pp.132-146
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    • 2017
  • Over the years(1971 to 1987), only 20 Korean film production companies had been granted the exclusive rights to make Korean films in Korea and to import foreign films with a quota system. They had been making trashy Korean movies to secure import quotas of foreign films. Newcomer's entry of market had also been limited and the growth of Korean films through innovation had been hampered. In the same period, The annual number of Rookie director is 10, the portion of debut films of all Korean films is 10.62%, and the audience portion of debut is only 10.5% of Korean movie audience. From 2004 to 2016, total number of rookie directors is 874, and 61.72% of 1,416 directors who made his debut over the 46 years from 1971 to 2016. This is far more than the number of directors who debuted for the last 32 years. From 2004 to 2016, the annual number of rookie directors is 62.15 and their debut film occupies 39.58% of the total amount of Korean movies released and 32.8% of the audience number. Since the full opening of the domestic market to foreign films industries in 1988, the liberalization of independent film production in 1999, as a result of innovation of a competitive system, the Korean movies have been loved by audiences. However, there are concerns that the decline of the screen quota in 2006, the monopoly of the 4 major distributors, increase in indie movies and Semi-adult movies for VOD, could be the potential threat for future innovation in Korean cinema.

Korean Academy of Film Arts(KAFA) as A Film Educational Institute (영화교육기관으로서의 한국영화아카데미)

  • Kim, Jung-Ho;Kim, Hak-Min
    • The Journal of the Korea Contents Association
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    • v.13 no.10
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    • pp.234-255
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    • 2013
  • Korean Academy of Film Arts (KAFA) is the film school run by Korean Film Council (KOFIC). KAFA was established in 1984, benchmarking American Film Institute (AFA) and in order to foster manpower for Korean Movie under the US's pressure of domestic movie market opening in Korea. The Korean Movie market was open to the world by 1987 and suffered from the low Korean-produced movie market share of around 20% in the domestic market from 1987 to 1998. During the last 30 years, KAFA plays the key role in the Korean movie making industry. Of 520 of number of their alumni, the number of directors is 101, 33 of cinematographers, 18 of producers and 21 of professors in universities' film departments. Korean Directors, Bong Joon-ho of (2013) and topped over 10 million domestic admissions to become the most-watched Korean films of all time. Now, with KAFA's relocation to Busan following with KOFIC, their new roles are promotion of the film industry in Busan, recruiting and educating new talented Korean and foreign student filmmakers, becoming an international film school.

Movie Box-office Prediction using Deep Learning and Feature Selection : Focusing on Multivariate Time Series

  • Byun, Jun-Hyung;Kim, Ji-Ho;Choi, Young-Jin;Lee, Hong-Chul
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.6
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    • pp.35-47
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    • 2020
  • Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.