论文标题
评估有关图像缩放的深图像质量评估的稳定性
Evaluating the Stability of Deep Image Quality Assessment With Respect to Image Scaling
论文作者
论文摘要
图像质量评估（IQA）是图像处理任务（例如压缩）的基本指标。使用了全参考iQA，使用了传统的智商，例如PSNR和SSIM。最近，还使用了基于深神经网络（深层IQA）的IQA，例如LPIPS和Dists。众所周知，图像缩放在深IQA中是不一致的，因为有些在预处理中执行缩放，而另一些则使用原始图像大小。在本文中，我们表明图像量表是影响深度IQA性能的影响因素。我们在同一五个数据集上全面评估了四个深IQA，实验结果表明，图像量表会显着影响IQA性能。我们发现，最合适的图像比例通常既不是默认尺寸也不是原始大小，并且选择取决于所使用的方法和数据集。我们可视化了稳定性，发现PIEAPP是四个深IQA中最稳定的。
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.
