Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image

• Published in: IEEE transactions on circuits and systems for video technology Vol. 30; no. 4; pp. 917 - 928
• Main Authors: Kim, Hak Gu, Lim, Heoun-Taek, Ro, Yong Man
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial learning; Deep learning; Distortion; Image coding; Image quality; Machine learning; Measurement; omnidirectional image; Perception; Performance evaluation; Quality; Quality assessment; Virtual networks; Virtual reality; Visualization

In this paper, we propose a novel deep learning-based virtual reality image quality assessment method that automatically predicts the visual quality of an omnidirectional image. In order to assess the visual quality in viewing the omnidirectional image, we propose deep networks consisting of virtual...