Quality Assessment for Omnidirectional Video: A Spatio-Temporal Distortion Modeling Approach

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Quality Assessment for Omnidirectional Video: A Spatio-Temporal Distortion Modeling Approach

By: 
Pan Gao; Pengwei Zhang; Aljosa Smolic

Omnidirectional video, also known as 360-degree video, has become increasingly popular nowadays due to its ability to provide immersive and interactive visual experiences. However, the ultra high resolution and the spherical observation space brought by the large spherical viewing range make omnidirectional video distinctly different from traditional 2D video. To date, the video quality assessment (VQA) for omnidirectional video is still an open issue. The existing VQA metrics for omnidirectional video only consider the spatial characteristics of distortions, but the temporal change of spatial distortions can also considerably influence human visual perception. In this paper, we propose a spatiotemporal modeling approach to evaluate the quality of the omnidirectional video. Firstly, we construct a spatioral quality assessment unit to evaluate the average distortion in temporal dimension at the eye fixation level, based upon which the smoothed distortion value is recursively calculated and consolidated by the characteristics of temporal variations. Then, we give a detailed solution of how to to integrate the three existing spatial VQA metrics into our approach. Besides, the cross-format omnidirectional video distortion measurement is also investigated. Finally, the spatiotemporal distortion of the whole video sequence is obtained by pooling. Based on the modeling approach, a full reference objective quality assessment metric for omnidirectional video is derived, namely OV-PSNR. The experimental results show that our proposed OV-PSNR greatly improves the prediction performance of the existing VQA metrics for omnidirectional video.

SPS on Twitter

  • CALL FOR PROPOSALS: Now seeking proposals for the 2024 IEEE International Workshop on Machine Learning for Signal P… https://t.co/l7V1bF2qhT
  • The DEGAS Webinar Series continues on Thursday, 19 May when Dr. Usman A. Khan presents "Distributed stochastic non-… https://t.co/AbfwVL0Yne
  • The IEEE Journal of Selected Topics in Signal Processing is now accepting submissions for a Special Issue on Signal… https://t.co/PbuzgYLigt
  • RT : New graduates transitioning to the next stage of their career often have several questions. In this video, I share… https://t.co/WA4aRlKNRn
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is accepting papers for a Special Issue… https://t.co/4RCWojWXO0

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar