Video Quality Assessment

We propose enhanced methods of video quality assessment (VQA) beyond the commonly used mean opinion scores by including factors such as similarity awareness, predicted eye movement sequences, and quantifying the perceptual viewing experience. Furthermore, we create a large video database that is diverse in content and authentic in distortions commonly encountered.

Project Members

Computer and Information Science



  • Masud Rana
  • Oliver Wiedemann

Project Description

The visual quality of images and videos is typically assessed by human experts and yields mean opinion scores (MOS). To reduce cost and time, providing methods for automated image/video quality assessment (IQA/VQA) is desirable for the multimedia and signal processing community at large. Current VQA methods are designed solely to capture aspects of the technical quality of displayed video streams. In addition to such visual quality we aim at methods to characterize images and videos in terms of other perceptual aspects. 

These aspects include the number and magnitude of eye movements required for viewing the content, the viewer's appreciation of the use of color, and the degree of interestingness. Several such components together with visual quality are combined for an overall ration of perceptual quality. Moreover, by investigating the human perceptual process and by understanding psychophysical phenomena, a saliency model will be developed that is based on a Markov model of eye movements.

Additionally, we will bring the state-of-the art a step closer to reality by setting up and applying media databases of authentic distortions and diverse content, which stand in contrast to current scientific data sets containing only a small variety of content and 'artificial' distortions.

Given an image or video whose visual quality is to be assessed, the question arises as to which IQA/VQA algorithm should be applied. Instead of choosing an algorithm based on a fixed test database, it can be assumed that a better quality assessment is possible when choosing an algorithm based on a particular test database consisting only of images/videos similar to the query image/video.

A number of problems occur:

  • What type of similarity is the most appropriate for this application? 
  • What statistical/perceptual features should be extracted to express similarity? 
  • How can the statistical/perceptual similarity of the input image and the test images be estimated? 
  • Should algorithms be combined to get more robust results?

Former Project Members

ResearchersStudentsStudent Assistants
  • Dr. Igor Zingman


Links and References

Financial Support:


Hosu, V., Hahn, F., Jenadeleh, M., Lin, H., Men, H., Szirányi, T., Li, S., Saupe, D., The Konstanz natural video database (KoNViD-1k), 9th International Conference on Quality of Multimedia Experience (QoMEX), 2017.

Men, H., Lin, H., Saupe, D., Empirical evaluation of no-reference VQA methods on a natural video quality database, 9th International Conference on Quality of Multimedia Experience (QoMEX), 2017.

Hosu, V., Hahn, F., Zingman, I., Saupe, D., Reported Attention as a Promising Alternative to Gaze in IQA Tasks, 5th International Workshop on Perceptual Quality of Systems 2016 (PQS 2016), Berlin, August 2016.

Saupe, D., Hahn, F., Hosu, V., Zingman, I., Rana, R., Li, S., Crowd workers proven useful: A comparative study of subjective video quality assessment, Eight International Workshop on Quality of Multimedia Experience (QoMEX 2016), Lisbon, June 2016.