ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression

@article{Chen2021ProxIQAAP,
  title={ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression},
  author={Li-Heng Chen and C. Bampis and Z. Li and A. Norkin and A. Bovik},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  volume={30},
  pages={360-373}
}
The use of $\ell _{p}$ (p = 1,2) norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess the loss of visual information, these simple norms are not very consistent with human perception. Here, we describe a different “proximal” approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, broadly termed ProxIQA, which mimics the… Expand
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