Unsupervised feature learning framework for no-reference image quality assessment

  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},
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
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