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
Bianco, S., Celona, L., Napoletano, P., & Schettini, R. (2018). On the use of deep learning for blind image quality assessment. SIGNAL, IMAGE AND VIDEO PROCESSING, 12(2), 355-362.
Citazione: | Bianco, S., Celona, L., Napoletano, P., & Schettini, R. (2018). On the use of deep learning for blind image quality assessment. SIGNAL, IMAGE AND VIDEO PROCESSING, 12(2), 355-362. |
Tipo: | Articolo in rivista - Articolo scientifico |
Carattere della pubblicazione: | Scientifica |
Presenza di un coautore afferente ad Istituzioni straniere: | No |
Titolo: | On the use of deep learning for blind image quality assessment |
Autori: | Bianco, S; Celona, L; Napoletano, P; Schettini, R |
Autori: | |
Data di pubblicazione: | 2018 |
Lingua: | English |
Rivista: | SIGNAL, IMAGE AND VIDEO PROCESSING |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/s11760-017-1166-8 |
Appare nelle tipologie: | 01 - Articolo su rivista |
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