Name
Abstract | ||
|---|---|---|
No-reference video quality metrics are becoming ever more popular, as they are more useful in real-life applications compared to full-reference metrics. Many proposed metrics extract features related to human perception from the individual video frames. Hence the video sequences have to be decoded first, before the metrics can be applied. In order to avoid decoding just for quality estimation, we therefore present in this contribution a no-reference metric for HDTV that uses features directly extracted from the H.264/AVC bitstream. We combine these features with the results from subjective tests using a data analysis approach with partial least squares regression to gain a prediction model for the visual quality. For verification, we performed a cross validation. Our results show that the proposed no-reference metric outperforms other metrics and delivers a correlation between the quality prediction and the actual quality of 0.93. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6116383 | Image Processing |
Keywords | Field | DocType |
feature extraction,high definition television,least squares approximations,regression analysis,H.264/AVC bitstream features,HDTV,data analysis approach,feature extraction,no-reference video quality metric,partial least squares regression,quality prediction,1080p25,H.264/AVC,HDTV,no-reference metric,subjective testing,video quality metric,visual quality | Computer vision,High-definition television,Pattern recognition,Computer science,Subjective video quality,Feature extraction,Artificial intelligence,Decoding methods,Bitstream,Video quality,Cross-validation,Scalable Video Coding | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4577-1302-6 | 978-1-4577-1302-6 | 13 |
PageRank | References | Authors |
0.95 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Keimel | 1 | 150 | 12.00 |
| Manuel Klimpke | 2 | 13 | 0.95 |
| Julian Habigt | 3 | 79 | 6.90 |
| Klaus Diepold | 4 | 437 | 56.47 |