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 [10.1007/s11760-017-1166-8].

On the use of deep learning for blind image quality assessment

Bianco, S;Celona, L.;Napoletano, P
;
Schettini, R
2018

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
Articolo in rivista - Articolo scientifico
Deep learning; Convolutional neural networks; Transfer learning; Blind image quality assessment; Perceptual image quality
English
2018
12
2
355
362
reserved
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 [10.1007/s11760-017-1166-8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/168489
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