Evaluating the Stability of Deep Image Quality Assessment With Respect to Image Scaling
This addresses a practical issue for researchers and practitioners using deep IQAs in image processing, but it is incremental as it evaluates existing methods rather than proposing new ones.
The paper tackled the problem of inconsistent image scaling in deep image quality assessment (IQA) methods, showing that image scale significantly influences performance, with PieAPP identified as the most stable among four evaluated deep IQAs.
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.