  Image quality assessment (IQA) is an important research topic for
understanding and improving visual experience. The current state-of-the-art IQA
methods are based on convolutional neural networks (CNNs). The performance of
CNN-based models is often compromised by the fixed shape constraint in batch
training. To accommodate this, the input images are usually resized and cropped
to a fixed shape, causing image quality degradation. To address this, we design
a multi-scale image quality Transformer (MUSIQ) to process native resolution
images with varying sizes and aspect ratios. With a multi-scale image
representation, our proposed method can capture image quality at different
granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale
embedding is proposed to support the positional embedding in the multi-scale
representation. Experimental results verify that our method can achieve
state-of-the-art performance on multiple large scale IQA datasets such as
PaQ-2-PiQ, SPAQ and KonIQ-10k.
