论文标题
适用性全参考图像质量的限制
Applicability limitations of differentiable full-reference image-quality
论文作者
论文摘要
主观图像质量测量在图像处理应用的开发中起着至关重要的作用。视觉质量度量的目的是近似主观评估的结果。在这方面，越来越多的指标正在开发中，但是很少的研究考虑了它们的局限性。本文解决了这种不足：我们在压缩前的图像预处理如何人为地提高流行的指标区，LPIPS，HAARPSI和VIF提供的质量分数，以及这些分数与主观品质得分的不一致。我们提出了一系列神经网络预处理模型，该模型将距离提高34.5％，LPIPS高达36.8％，VIF高达98.0％，而Haarpsi则在JPEG压缩图像的情况下增加了22.6％。对预处理图像的主观比较表明，对于我们检查的大多数指标，视觉质量下降或保持不变，从而限制了这些指标的适用性。
Subjective image-quality measurement plays a critical role in the development of image-processing applications. The purpose of a visual-quality metric is to approximate the results of subjective assessment. In this regard, more and more metrics are under development, but little research has considered their limitations. This paper addresses that deficiency: we show how image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF as well as how these scores are inconsistent with subjective-quality scores. We propose a series of neural-network preprocessing models that increase DISTS by up to 34.5%, LPIPS by up to 36.8%, VIF by up to 98.0%, and HaarPSI by up to 22.6% in the case of JPEG-compressed images. A subjective comparison of preprocessed images showed that for most of the metrics we examined, visual quality drops or stays unchanged, limiting the applicability of these metrics.
