Signal, Image and Video Processing

, Volume 12, Issue 2, pp 355–362 | Cite as

On the use of deep learning for blind image quality assessment

  • Simone Bianco
  • Luigi Celona
  • Paolo NapoletanoEmail author
  • Raimondo Schettini
Original Paper


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.


Deep learning Convolutional neural networks Transfer learning Blind image quality assessment Perceptual image quality 


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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly

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