Abstract
For high bit rate video, it is important to acquire the video contents with high resolution, the quality of which may be degraded due to the motion blur from the movement of an object(s) or the camera. However, conventional sharpness assessments are designed to find focal blur caused either by defocusing or by compression distortion targeted for low bit rates. To overcome this limitation, we present a no-reference framework of a visual sharpness assessment (VSA) for high-resolution video based on the motion and scene classification. In the proposed framework, the accuracy of the sharpness estimation can be improved via pooling weighted by the visual perception from the object and camera movements and by the strong influence from the region with the highest sharpness. Based on the motion blur characteristics, the variance and the contrast over the spectral domain are used to quantify the perceived sharpness. Moreover, for the VSA, we extract the highly influential sharper regions and emphasize them by utilizing the scene adaptive pooling.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE International Conference on Multimedia and Expo, ICME 2015 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781479970827 |
| DOIs | |
| Publication status | Published - 2015 Aug 4 |
| Event | IEEE International Conference on Multimedia and Expo, ICME 2015 - Turin, Italy Duration: 2015 Jun 29 → 2015 Jul 3 |
Publication series
| Name | Proceedings - IEEE International Conference on Multimedia and Expo |
|---|---|
| Volume | 2015-August |
| ISSN (Print) | 1945-7871 |
| ISSN (Electronic) | 1945-788X |
Other
| Other | IEEE International Conference on Multimedia and Expo, ICME 2015 |
|---|---|
| Country | Italy |
| City | Turin |
| Period | 15/6/29 → 15/7/3 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
Cite this
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Video sharpness prediction based on motion blur analysis. / Kim, Jongyoo; Kim, Junghwan; Kim, Woojae; Lee, Jisoo; Lee, Sanghoon.
2015 IEEE International Conference on Multimedia and Expo, ICME 2015. IEEE Computer Society, 2015. 7177424 (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2015-August).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Video sharpness prediction based on motion blur analysis
AU - Kim, Jongyoo
AU - Kim, Junghwan
AU - Kim, Woojae
AU - Lee, Jisoo
AU - Lee, Sanghoon
PY - 2015/8/4
Y1 - 2015/8/4
N2 - For high bit rate video, it is important to acquire the video contents with high resolution, the quality of which may be degraded due to the motion blur from the movement of an object(s) or the camera. However, conventional sharpness assessments are designed to find focal blur caused either by defocusing or by compression distortion targeted for low bit rates. To overcome this limitation, we present a no-reference framework of a visual sharpness assessment (VSA) for high-resolution video based on the motion and scene classification. In the proposed framework, the accuracy of the sharpness estimation can be improved via pooling weighted by the visual perception from the object and camera movements and by the strong influence from the region with the highest sharpness. Based on the motion blur characteristics, the variance and the contrast over the spectral domain are used to quantify the perceived sharpness. Moreover, for the VSA, we extract the highly influential sharper regions and emphasize them by utilizing the scene adaptive pooling.
AB - For high bit rate video, it is important to acquire the video contents with high resolution, the quality of which may be degraded due to the motion blur from the movement of an object(s) or the camera. However, conventional sharpness assessments are designed to find focal blur caused either by defocusing or by compression distortion targeted for low bit rates. To overcome this limitation, we present a no-reference framework of a visual sharpness assessment (VSA) for high-resolution video based on the motion and scene classification. In the proposed framework, the accuracy of the sharpness estimation can be improved via pooling weighted by the visual perception from the object and camera movements and by the strong influence from the region with the highest sharpness. Based on the motion blur characteristics, the variance and the contrast over the spectral domain are used to quantify the perceived sharpness. Moreover, for the VSA, we extract the highly influential sharper regions and emphasize them by utilizing the scene adaptive pooling.
UR - http://www.scopus.com/inward/record.url?scp=84946085688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946085688&partnerID=8YFLogxK
U2 - 10.1109/ICME.2015.7177424
DO - 10.1109/ICME.2015.7177424
M3 - Conference contribution
AN - SCOPUS:84946085688
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2015 IEEE International Conference on Multimedia and Expo, ICME 2015
PB - IEEE Computer Society
ER -