  Recently, deep convolutional neural networks (DCNN) that leverage the
adversarial training framework for image restoration and enhancement have
significantly improved the processed images' sharpness. Surprisingly, although
these DCNNs produced crispier images than other methods visually, they may get
a lower quality score when popular measures are employed for evaluating them.
Therefore it is necessary to develop a quantitative metric to reflect their
performances, which is well-aligned with the perceived quality of an image.
Famous quantitative metrics such as Peak signal-to-noise ratio (PSNR), The
structural similarity index measure (SSIM), and Perceptual Index (PI) are not
well-correlated with the mean opinion score (MOS) for an image, especially for
the neural networks trained with adversarial loss functions.
  This paper has proposed a convolutional neural network using an extension
architecture of the traditional Siamese network so-called Siamese-Difference
neural network. We have equipped this architecture with the spatial and
channel-wise attention mechanism to increase our method's performance.
  Finally, we employed an auxiliary loss function to train our model. The
suggested additional cost function surrogates ranking loss to increase
Spearman's rank correlation coefficient while it is differentiable concerning
the neural network parameters. Our method achieved superior performance in
\textbf{\textit{NTIRE 2021 Perceptual Image Quality Assessment}} Challenge. The
implementations of our proposed method are publicly available.
