Name
Abstract | ||
|---|---|---|
In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a linear correlation coefficient with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008, and TID2013. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11760-017-1166-8 | Signal, Image and Video Processing |
Keywords | DocType | Volume |
Deep learning,Convolutional neural networks,Transfer learning,Blind image quality assessment,Perceptual image quality | Journal | abs/1602.05531 |
Issue | ISSN | Citations |
2 | 1863-1703 | 43 |
PageRank | References | Authors |
1.19 | 40 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Bianco | 1 | 226 | 24.48 |
| Luigi Celona | 2 | 66 | 7.70 |
| Paolo Napoletano | 3 | 339 | 37.19 |
| Raimondo Schettini | 4 | 1476 | 154.06 |