论文标题
对KADID-10K数据库中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 KADID-10k database
论文作者
论文摘要
客观图像质量评估的主要目标是设计计算，数学模型，这些模型能够与主观评估一致地预测感知图像质量。客观图像质量评估算法的评估是基于对公开可用基准数据库进行的实验。在这项研究中，我们的目标是使用最近发表的最近发布的KADID-10K数据库，对无参考图像质量评估算法进行全面评估，其原始源代码可在线获得，该数据库是最大的可用基准数据库之一。具体而言，据报道，平均PLCC，SROCC和KROCC测量了100次随机火车测试分裂。此外，将数据库分为火车（图像的80 \％）和相对于参考图像的测试集（图像的20％）。因此，这两组之间没有语义内容重叠。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。
The main goal of objective image quality assessment is to devise computational, mathematical models which are able to predict perceptual image quality consistently with subjective evaluations. The evaluation of objective image quality assessment algorithms is based on experiments conducted on publicly available benchmark databases. In this study, our goal is to give a comprehensive evaluation about no-reference image quality assessment algorithms, whose original source codes are available online, using the recently published KADID-10k database which is one of the largest available benchmark databases. Specifically, average PLCC, SROCC, and KROCC are reported which were measured over 100 random train-test splits. Furthermore, the database was divided into a train (appx. 80\% of images) and a test set (appx. 20% of images) with respect to the reference images. So no semantic content overlap was between these two sets. 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.
