  Image quality assessment is a fundamental problem in the field of image
processing, and due to the lack of reference images in most practical
scenarios, no-reference image quality assessment (NR-IQA), has gained
increasing attention recently. With the development of deep learning
technology, many deep neural network-based NR-IQA methods have been developed,
which try to learn the image quality based on the understanding of database
information. Currently, Transformer has achieved remarkable progress in various
vision tasks. Since the characteristics of the attention mechanism in
Transformer fit the global perceptual impact of artifacts perceived by a human,
Transformer is thus well suited for image quality assessment tasks. In this
paper, we propose a Transformer based NR-IQA model using a predicted objective
error map and perceptual quality token. Specifically, we firstly generate the
predicted error map by pre-training one model consisting of a Transformer
encoder and decoder, in which the objective difference between the distorted
and the reference images is used as supervision. Then, we freeze the parameters
of the pre-trained model and design another branch using the vision Transformer
to extract the perceptual quality token for feature fusion with the predicted
error map. Finally, the fused features are regressed to the final image quality
score. Extensive experiments have shown that our proposed method outperforms
the current state-of-the-art in both authentic and synthetic image databases.
Moreover, the attentional map extracted by the perceptual quality token also
does conform to the characteristics of the human visual system.
