  In this paper, we propose an image quality transformer (IQT) that
successfully applies a transformer architecture to a perceptual full-reference
image quality assessment (IQA) task. Perceptual representation becomes more
important in image quality assessment. In this context, we extract the
perceptual feature representations from each of input images using a
convolutional neural network (CNN) backbone. The extracted feature maps are fed
into the transformer encoder and decoder in order to compare a reference and
distorted images. Following an approach of the transformer-based vision models,
we use extra learnable quality embedding and position embedding. The output of
the transformer is passed to a prediction head in order to predict a final
quality score. The experimental results show that our proposed model has an
outstanding performance for the standard IQA datasets. For a large-scale IQA
dataset containing output images of generative model, our model also shows the
promising results. The proposed IQT was ranked first among 13 participants in
the NTIRE 2021 perceptual image quality assessment challenge. Our work will be
an opportunity to further expand the approach for the perceptual IQA task.
