Special Section on Image/Video Quality and System Performance

Video quality assessment using visual attention computational models

[+] Author Affiliations
Welington Y. L. Akamine

University of Brasília (UnB), Department of Electrical Engineering, Campus Universitário Darcy Ribeiro, 70919-970 Brasília—DF, Brazil

Mylène C. Q. Farias

University of Brasília (UnB), Department of Electrical Engineering, Campus Universitário Darcy Ribeiro, 70919-970 Brasília—DF, Brazil

J. Electron. Imaging. 23(6), 061107 (Sep 05, 2014). doi:10.1117/1.JEI.23.6.061107
History: Received April 1, 2014; Revised July 10, 2014; Accepted August 13, 2014
Text Size: A A A

Abstract.  A recent development in the area of image and video quality consists of trying to incorporate aspects of visual attention in the design of visual quality metrics, mostly using the assumption that visual distortions appearing in less salient areas might be less visible and, therefore, less annoying. This research area is still in its infancy and results obtained by different groups are not yet conclusive. Among the works that have reported some improvements, most use subjective saliency maps, i.e., saliency maps generated from eye-tracking data obtained experimentally. Other works address the image quality problem, not focusing on how to incorporate visual attention into video signals. We investigate the benefits of incorporating bottom-up video saliency maps (obtained using Itti’s computational model) into video quality metrics. In particular, we compare the performance of four full-reference video quality metrics with their modified versions, which had saliency maps incorporated into the algorithm. Results show that the addition of video saliency maps improve the performance of most quality metrics tested, but the highest gains were obtained for the metrics that only took into consideration spatial degradations.

Figures in this Article
© 2014 SPIE and IS&T

Citation

Welington Y. L. Akamine and Mylène C. Q. Farias
"Video quality assessment using visual attention computational models", J. Electron. Imaging. 23(6), 061107 (Sep 05, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.061107


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement


 

  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.