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

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

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Attention modeling for video quality assessment : balancing global quality and local quality. / You, Junyong; Korhonen, Jari; Perkis, Andrew.

proceedings ICME. IEEE, 2010. p. 914-919.

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

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APA

You, J., Korhonen, J., & Perkis, A. (2010). Attention modeling for video quality assessment: balancing global quality and local quality. In proceedings ICME. (pp. 914-919). IEEE.

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Author

You, Junyong; Korhonen, Jari; Perkis, Andrew / Attention modeling for video quality assessment : balancing global quality and local quality.

proceedings ICME. IEEE, 2010. p. 914-919.

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

Bibtex

@inbook{713481d93ba84bab8f51761e17b034b3,
title = "Attention modeling for video quality assessment",
publisher = "IEEE",
author = "Junyong You and Jari Korhonen and Andrew Perkis",
year = "2010",
isbn = "978-1-4244-7492-9",
pages = "914-919",
booktitle = "proceedings ICME",

}

RIS

TY - GEN

T1 - Attention modeling for video quality assessment

T2 - proceedings ICME

A1 - You,Junyong

A1 - Korhonen,Jari

A1 - Perkis,Andrew

AU - You,Junyong

AU - Korhonen,Jari

AU - Perkis,Andrew

PB - IEEE

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Visual attention

KW - Motion

KW - Quality metric

KW - Temporal pooling

KW - Saliency

UR - http://www.icme2010.org

SN - 978-1-4244-7492-9

BT - proceedings ICME

SP - 914

EP - 919

ER -