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TextureToMTF: predicting spatial frequency response in the wild

Abstract

In this work, we propose an no-reference image quality assessment (NR-IQA) approach at a confluence of signal processing and deep learning. We use MTF50 (spatial frequency where modulation transfer function is 50% of its peak value) on slanted edged as a measure for image quality. We propose a comprehensive IQA dataset of images captured through hand-held phone camera in variety of situations with slanted edges around it. The MTF50 values at the slanted edges are then used to garner ground truth values for each patch in the captured images. A convolution neural network is then trained to predict MTF50 values from arbitrary image patches. We present results on the proposed dataset and synthetically generated TID2013 dataset and show state-of-the-art performance for IQA in the wild.

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    https://murtuzabohra.github.io/TextureToMTF/.

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Correspondence to Murtuza Bohra.

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Bohra, M., Maheshwari, S. & Gandhi, V. TextureToMTF: predicting spatial frequency response in the wild. SIViP (2020). https://doi.org/10.1007/s11760-020-01656-w

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Keywords

  • Image quality prediction
  • Blur prediction
  • Image sharpness
  • Spatial frequency