Attention modeling for video quality assessment: balancing global quality and local quality

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2010



View graph of relations

This paper proposes to evaluate video quality by balancing two quality components: global quality and local quality. The global quality is a result from subjects allocating their ttention equally to all regions in a frame and all frames n a video. It is evaluated by image quality metrics (IQM) ith averaged spatiotemporal pooling. The local quality is derived from visual attention modeling and quality variations over frames. Saliency, motion, and contrast information are taken into account in modeling visual attention, which is then integrated into IQMs to calculate the local quality of a video frame. The local quality of a video sequence is calculated by pooling local quality values over all frames with a temporal pooling scheme derived from the known relationship between perceived video quality and the frequency of temporal quality variations. The overall quality of a distorted video is a weighted average between the global quality and the local quality. Experimental results demonstrate that the combination of the global quality and local quality outperforms both sole global quality and local quality, as well as other quality models, in video quality assessment. In addition, the proposed video quality modeling algorithm can improve the performance of image quality metrics on video quality assessment compared to the normal averaged spatiotemporal pooling scheme.
Original languageEnglish
Title of host publicationproceedings ICME
Publication date2010
ISBN (print)978-1-4244-7492-9
StatePublished - 2010
EventInternational Conference on Multimedia and EXPO - Singapore


ConferenceInternational Conference on Multimedia and EXPO
Period01/01/2010 → …


  • Visual attention, Motion, Quality metric, Temporal pooling, Saliency
Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

Download statistics

No data available

ID: 4598594