Rating realism assessment for computer generated imagery

Wen Jung Huang, Chia Hung Yeh, Chia Chen Kuo, Yuan Chen Cheng, Jia Ying Lin

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

Computer-generated imagery (CGI) is becoming integral to a movie's story and appeal, and even dominates the film's success at box office. Currently the CGI realism is evaluated by post-production supervisors, and few objective realism assessments focus on this area. This paper investigates enhanced feature learning and classifier training for CGI assessment by deep learning. A training-set-selection method is proposed to select proper samples, which is crucial to deep learning training. Then the selected samples are converted into entropy images with enhanced features. We adopt a convolutional neural network for feature learning and classifier training to estimate the realism of CGI. Experimental results show that the developed matric has acceptable accuracy when compared to the grout truth. In addition, the rating result of the proposed assessment is very close to that of human visual perception.

原文英語
主出版物標題2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面327-328
頁數2
ISBN(電子)9781509040179
DOIs
出版狀態已發佈 - 2017 七月 25
事件4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017 - Taipei, 美国
持續時間: 2017 六月 122017 六月 14

出版系列

名字2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017

其他

其他4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
國家/地區美国
城市Taipei
期間2017/06/122017/06/14

ASJC Scopus subject areas

  • 電腦視覺和模式識別
  • 電腦網路與通信
  • 訊號處理
  • 生物醫學工程
  • 儀器
  • 電氣與電子工程

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