By Topic

Considering Temporal Variations of Spatial Visual Distortions in Video Quality Assessment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

The temporal distortions such as flickering, jerkiness, and mosquito noise play a fundamental part in video quality assessment. A temporal distortion is commonly defined as the temporal evolution, or fluctuation, of the spatial distortion on a particular area which corresponds to the image of a specific object in the scene. Perception of spatial distortions over time can be largely modified by their temporal changes, such as increase or decrease in the distortions, or as periodic changes in the distortions. In this paper, we have designed a perceptual full reference video quality assessment metric by focusing on the temporal evolutions of the spatial distortions. As the perception of the temporal distortions is closely linked to the visual attention mechanisms, we have chosen to first evaluate the temporal distortion at eye fixation level. In this short-term temporal pooling, the video sequence is divided into spatio-temporal segments in which the spatio-temporal distortions are evaluated, resulting in spatio-temporal distortion maps. Afterwards, the global quality score of the whole video sequence is obtained by the long-term temporal pooling in which the spatio-temporal maps are spatially and temporally pooled. Consistent improvement over objective existing video quality assessment methods is observed. Our validation has been realized with a dataset built from video sequences of various contents.

Published in:

IEEE Journal of Selected Topics in Signal Processing  (Volume:3 ,  Issue: 2 )