  No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual
quality of images in accordance with human subjective perception.
Unfortunately, existing NR-IQA methods are far from meeting the needs of
predicting accurate quality scores on GAN-based distortion images. To this end,
we propose Multi-dimension Attention Network for no-reference Image Quality
Assessment (MANIQA) to improve the performance on GAN-based distortion. We
firstly extract features via ViT, then to strengthen global and local
interactions, we propose the Transposed Attention Block (TAB) and the Scale
Swin Transformer Block (SSTB). These two modules apply attention mechanisms
across the channel and spatial dimension, respectively. In this
multi-dimensional manner, the modules cooperatively increase the interaction
among different regions of images globally and locally. Finally, a dual branch
structure for patch-weighted quality prediction is applied to predict the final
score depending on the weight of each patch's score. Experimental results
demonstrate that MANIQA outperforms state-of-the-art methods on four standard
datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our
method ranked first place in the final testing phase of the NTIRE 2022
Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and
models are available at https://github.com/IIGROUP/MANIQA.
