论文标题
对真实扭曲的无参考图像质量评估算法的全面评估
Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions
论文作者
论文摘要
客观图像质量评估涉及数字图像感知质量的预测。无参考图像质量评估可以预测给定输入图像的质量，而没有任何有关其原始（无失真）对应物的知识或信息。机器学习算法大量用于无引用图像质量评估，因为对人类视觉系统的质量感知进行建模非常复杂。此外，在公开可用的基准数据库中评估了无参考图像质量评估算法。这些数据库包含及其相应质量分数的图像。在这项研究中，我们评估了几种基于机器学习的NR-IQA方法，并且在包含真实扭曲的数据库上的一种不知道的方法。具体而言，应用于野外和KONIQ-10K数据库中的生活，以评估最先进的方法。对于基于机器学习的方法，appx。 80％用于训练，其余20％用于测试。此外，据报道，平均PLCC，SROCC和KROCC值超过100次随机火车测试拆分。 PLCC，SROCC和KROCC值的统计数据也使用BoxPlot发表。我们的评估结果可能有助于了解最新的无参考图像质量评估方法的状况。
Objective image quality assessment deals with the prediction of digital images' perceptual quality. No-reference image quality assessment predicts the quality of a given input image without any knowledge or information about its pristine (distortion free) counterpart. Machine learning algorithms are heavily used in no-reference image quality assessment because it is very complicated to model the human visual system's quality perception. Moreover, no-reference image quality assessment algorithms are evaluated on publicly available benchmark databases. These databases contain images with their corresponding quality scores. In this study, we evaluate several machine learning based NR-IQA methods and one opinion unaware method on databases consisting of authentic distortions. Specifically, LIVE In the Wild and KonIQ-10k databases were applied to evaluate the state-of-the-art. For machine learning based methods, appx. 80% were used for training and the remaining 20% were used for testing. Furthermore, average PLCC, SROCC, and KROCC values were reported over 100 random train-test splits. The statistics of PLCC, SROCC, and KROCC values were also published using boxplots. Our evaluation results may be helpful to obtain a clear understanding about the status of state-of-the-art no-reference image quality assessment methods.
