Unsupervised feature learning framework for no-reference image quality assessment

@article{Ye2012UnsupervisedFL,
  title={Unsupervised feature learning framework for no-reference image quality assessment},
  author={Peng Ye and Jayant Kumar and Le Kang and David S. Doermann},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={1098-1105}
}
In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to build a computational model to automatically predict human perceived image quality without a reference image and without knowing the distortion present in the image. Previous approaches for this problem typically rely on hand-crafted features which are carefully designed based on prior knowledge. In contrast, we use… CONTINUE READING
Highly Influential
This paper has highly influenced 63 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 352 citations. REVIEW CITATIONS
184 Citations
32 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 184 extracted citations

352 Citations

050100'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 352 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 32 references

An analysis of single-layer networks in unsupervised feature learning

  • A. Coates, H. Lee, A. Y. Ng
  • Proceedings of the 14th International Conference…
  • 2011
1 Excerpt

Similar Papers

Loading similar papers…