Visual attention guided bit allocation in video compression
A visual attention-based bit allocation strategy for video compression is proposed. Saliency-based attention prediction is used to detect interesting regions in video. From the top salient locations from the computed saliency map, a guidance map is generated to guide the bit allocation strategy thro...
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| Published in | Image and vision computing Vol. 29; no. 1; pp. 1 - 14 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2011
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| Subjects | |
| Online Access | Get full text |
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| Abstract | A visual attention-based bit allocation strategy for video compression is proposed. Saliency-based attention prediction is used to detect interesting regions in video. From the top salient locations from the computed saliency map, a guidance map is generated to guide the bit allocation strategy through a new constrained global optimization approach, which can be solved in a closed form and independently of video frame content. Fifty video sequences (300 frames each) and eye-tracking data from 14 subjects were collected to evaluate both the accuracy of the attention prediction model and the subjective quality of the encoded video. Results show that the area under the curve of the guidance map is 0.773
±
0.002, significantly above chance (0.500). Using a new eye-tracking-weighted PSNR (EWPSNR) measure of subjective quality, more than 90% of the encoded video clips with the proposed method achieve better subjective quality compared to standard encoding with matched bit rate. The improvement in EWPSNR is up to over 2
dB and on average 0.79
dB. |
|---|---|
| AbstractList | A visual attention-based bit allocation strategy for video compression is proposed. Saliency-based attention prediction is used to detect interesting regions in video. From the top salient locations from the computed saliency map, a guidance map is generated to guide the bit allocation strategy through a new constrained global optimization approach, which can be solved in a closed form and independently of video frame content. Fifty video sequences (300 frames each) and eye-tracking data from 14 subjects were collected to evaluate both the accuracy of the attention prediction model and the subjective quality of the encoded video. Results show that the area under the curve of the guidance map is 0.773
±
0.002, significantly above chance (0.500). Using a new eye-tracking-weighted PSNR (EWPSNR) measure of subjective quality, more than 90% of the encoded video clips with the proposed method achieve better subjective quality compared to standard encoding with matched bit rate. The improvement in EWPSNR is up to over 2
dB and on average 0.79
dB. |
| Author | Qin, Shiyin Li, Zhicheng Itti, Laurent |
| Author_xml | – sequence: 1 givenname: Zhicheng surname: Li fullname: Li, Zhicheng organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing, China – sequence: 2 givenname: Shiyin surname: Qin fullname: Qin, Shiyin organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing, China – sequence: 3 givenname: Laurent surname: Itti fullname: Itti, Laurent email: itti@pollux.usc.edu organization: Computer Science Department, University of Southern California, Los Angeles, CA, USA |
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| Keywords | Video subjective quality Video compression Visual attention Eye-tracking |
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| SubjectTerms | Eye-tracking Video compression Video subjective quality Visual attention |
| Title | Visual attention guided bit allocation in video compression |
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