On the use of deep learning for blind image quality assessment

• Published in: Signal, image and video processing Vol. 12; no. 2; pp. 355 - 362
• Main Authors: Bianco, Simone, Celona, Luigi, Napoletano, Paolo, Schettini, Raimondo
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2018; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Imaging; Computer Science; Correlation coefficients; Deep learning; Digital cameras; Feature extraction; Image Processing and Computer Vision; Image quality; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Quality; Quality assessment; Signal,Image and Speech Processing; Vision; Deep learning; Blind image quality assessment; Perceptual image quality; Convolutional neural networks; Transfer learning
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-017-1166-8

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.