Corpus ID: 195766823

Learning to Blindly Assess Image Quality in the Laboratory and Wild

@article{Zhang2019LearningTB,
  title={Learning to Blindly Assess Image Quality in the Laboratory and Wild},
  author={Weixia Zhang and K. Ma and X. Yang},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.00516}
}
  • Weixia Zhang, K. Ma, X. Yang
  • Published 2019
  • Computer Science, Engineering
  • ArXiv
  • Previous models for blind image quality assessment (BIQA) can only be trained (or fine-tuned) on one subject-rated database due to the difficulty of combining multiple databases with different perceptual scales. As a result, models trained in a well-controlled laboratory environment with synthetic distortions fail to generalize to realistic distortions, whose data distribution is different. Similarly, models optimized for images captured in the wild do not account for images simulated in the… CONTINUE READING

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