  Image quality assessment (IQA) is a fundamental metric for image processing
tasks (e.g., compression). With full-reference IQAs, traditional IQAs, such as
PSNR and SSIM, have been used. Recently, IQAs based on deep neural networks
(deep IQAs), such as LPIPS and DISTS, have also been used. It is known that
image scaling is inconsistent among deep IQAs, as some perform down-scaling as
pre-processing, whereas others instead use the original image size. In this
paper, we show that the image scale is an influential factor that affects deep
IQA performance. We comprehensively evaluate four deep IQAs on the same five
datasets, and the experimental results show that image scale significantly
influences IQA performance. We found that the most appropriate image scale is
often neither the default nor the original size, and the choice differs
depending on the methods and datasets used. We visualized the stability and
found that PieAPP is the most stable among the four deep IQAs.
