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Cited by 1,270

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Convolutional Neural Networks for No-Reference Image Quality Assessment

Kang, Le, Ye, Peng, Li, Yi, Doermann, David·2014·2014 IEEE Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2014.224
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Abstract

In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.

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Show all 4 references

Research works printed but not linked: 18

  1. 10.1109/TIP.2013.2293423 · DOI · Paper page
  2. 10.1109/TIP.2011.2109730 · DOI · Paper page
  3. 10.1109/TIP.2010.2092435 · DOI · Paper page
  4. 10.1109/CVPR.2013.133 · DOI · Paper page
  5. sheikh, 0, LIVE Image Quality Assessment Database Release 2
  6. 10.1109/TIP.2012.2191563 · DOI · Paper page
  7. 10.1109/TIP.2005.859389 · DOI · Paper page
  8. nair, 2010, Rectified linear units improve restricted boltzmann machines, ICML, 807
  9. ponomarenko, 2009, TID2008-a database for evaluation of full-reference visual quality assessment metrics, Advances of Modern Radio Electronics, 10, 30
  10. 10.1109/CVPR.2013.132 · DOI
  11. chetouani, 2010, A novel free reference image quality metric using neural network approach, 2nd Int Workshop Video Process Quality Metrics Consum Electron, 1
  12. ye, 2012, Unsupervised feature learning framework for no-reference image quality assessment, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1098
  13. bergstra, 2010, Theano: A CPU and GPU math expression compiler, Proceedings of the Python for Scientific Computing Conference (SciPy), 10.25080/Majora-92bf1922-003 · DOI · Paper page
  14. 10.1109/TPAMI.2013.50 · DOI · Paper page
  15. 10.1109/TIP.2011.2147325 · DOI · Paper page
  16. kavukcuoglu, 2010, Learning convolutional feature hierachies for visual recognition, Advances in Neural Information Processing Systems (NIPS)
  17. 10.1109/CVPR.2012.6248110 · DOI · Paper page
  18. 10.1109/TNN.2011.2120620 · DOI · Paper page

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Metadata

Authors
Kang, L., Ye, P., Li, Y., & Doermann, D.
Venue
2014 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2014-06
Pages
1733-1740
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2014.224
OpenAlex
W2051596736
Cited by
1,270
References
4
FWCI
28.47
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Convolutional Neural Networks for No-Reference Image Quality Assessment. https://app.cochat.ai/papers/10.1109/cvpr.2014.224
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Kang, L., Ye, P., Li, Y., & Doermann, D. (2014). Convolutional Neural Networks for No-Reference Image Quality Assessment. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.224

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