Deep Learning for Blind Image Quality Assessment

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 sub-regions of the original image. The score of each sub-region is computed using a Support Vector Regression (SVR) machine taking as input features extracted using a CNN fine-tuned for category-based image quality assessment. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, including those based on deep learning, having a Linear Correlation Coefficient (LCC) 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. Furthermore, in most of the cases, the quality score predictions of DeepBIQ are closer to the average observer than those of a generic human observer.

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On the use of deep learning for blind image quality assessment
(Simone Bianco, Luigi Celona, Paolo Napoletano, Raimondo Schettini) In Signal, Image and Video Processing, volume 12, number 2, pp. 355-362, 2018.

 author = {Bianco, Simone and Celona, Luigi and Napoletano, Paolo and Schettini, Raimondo},
 year = {2018},
 month = {2},
 year = {2018},
 pages = {355-362},
 title = {On the use of deep learning for blind image quality assessment},
 volume = {12},
 number = {2},
 journal = {Signal, Image and Video Processing},
 url = {},
 pdf = {},
 doi = {10.1007/s11760-017-1166-8},
 issn = {1863-1711},
 projectref = {}}