论文标题
无参考功能可以帮助全参考图像质量估计吗？
Can No-reference features help in Full-reference image quality estimation?
论文作者
论文摘要
感知图像质量评估（IQA）指标的开发对计算机视觉社区引起了重大兴趣。这些指标的目的是建模人类所感知的图像的质量。全参考IQA研究中的最新作品在对应于查询图像和参考图像以进行质量预测的深度特征之间进行了像素方面的比较。但是，如果查询图像中存在的失真很严重，那么Pixelwise特征比较可能没有意义。在这种情况下，我们探讨了全参考IQA任务中无参考功能的利用。我们的模型包括全参考和无参考分支。全参考分支使用扭曲和参考图像，而无参考分支仅使用扭曲的图像。我们的实验表明，使用无引用功能可以提高图像质量评估的性能。与KADID-10K和PIPAL数据集上的许多最新算法相比，我们的模型获得更高的SRCC和KRCC得分。
Development of perceptual image quality assessment (IQA) metrics has been of significant interest to computer vision community. The aim of these metrics is to model quality of an image as perceived by humans. Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction. However, pixelwise feature comparison may not be meaningful if distortion present in query image is severe. In this context, we explore utilization of no-reference features in Full-reference IQA task. Our model consists of both full-reference and no-reference branches. Full-reference branches use both distorted and reference images, whereas No-reference branch only uses distorted image. Our experiments show that use of no-reference features boosts performance of image quality assessment. Our model achieves higher SRCC and KRCC scores than a number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.
