An abstraction and structural information based depth perception evaluation metric Özetleme ve Yapi Bilgisine Dayanan Bir Derinlik Algisi Deǧerlendirme Metriǧi

Nur Yılmaz G., Bayrak H.

25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Turkey, 15 - 18 May 2017 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2017.7960722
  • City: Antalya
  • Country: Turkey
  • Keywords: 3D video, abstraction filter, depth perception, Reduced Reference (RR) metric, structural complexity
  • TED University Affiliated: No


© 2017 IEEE.Developing reliable and efficient 3 Dimensional (3D) video depth perception evaluation metrics is currently a trending research topic for supporting the advancement of the 3D video services. This support can be proliferated by utilizing effective 3D video features while modeling these metrics. In this study, a Reduced Reference (RR) depth perception evaluation metric using significant depth level and structural information as effective 3D video features is developed. The significant depth level and structural information in the Depth Maps (DM) are determined using abstraction filter and Canny edge detection algorithm, respectively. The performance assessment results of the proposed RR metric present that it is quite effective for ensuring advanced 3D video services.