  Deep networks have demonstrated promising results in the field of Image
Quality Assessment (IQA). However, there has been limited research on
understanding how deep models in IQA work. This study introduces a novel
positional masked transformer for IQA and provides insights into the
contribution of different regions of an image towards its overall quality.
Results indicate that half of an image may play a trivial role in determining
image quality, while the other half is critical. This observation is extended
to several other CNN-based IQA models, revealing that half of the image regions
can significantly impact the overall image quality. To further enhance our
understanding, three semantic measures (saliency, frequency, and objectness)
were derived and found to have high correlation with the importance of image
regions in IQA.
