基于多任务学习的无参考超分辨图像质量评估

No reference super resolution image quality assessment based on multi-task learning

图像超分辨率重建旨在从低分辨率图像中恢复其对应的高分辨率图像,是计算机视觉中的经典问题。为改进传统超分辨图像质量评价方法与人眼感知不一致的问题,提出一种基于多任务学习的超分辨图像质量评估网络。网络采用多任务学习的方式,分别学习图像的局部频率特征与质量分数,其中局部频率特征用来辅助网络进行图像质量分数的回归,提高分数预测的准确性和泛化能力。另外,在网络中加入协调注意力模块,进一步增强了模型的预测能力。实验结果表明,所提出的算法在QADS数据集上的SROCC、PLCC等指标优于目前先进的无参考超分辨图像质量评价方法。

Image super-resolution reconstruction is to recover the corresponding high resolution images from low resolution images,which is a classic problem in computer vision.In order to improve the inconsistency between traditional super-resolution image quality evaluation methods and visual perception,a super-resolution image quality evaluation network based on multi-task learning is proposed.The network adopts a multi-task learning method to learn the local frequency features and quality scores of the image respectively.The local frequency features are used to assist the network in the regression of the image quality scores to improve the accuracy and generalization ability of score prediction.In addition,adding coordinate attention blocks to the network to further enhance the predictive ability of the model.The experimental results show that the SROCC and PLCC of the proposed algorithm on the QADS dataset are better than the current advanced no-reference super-resolution image quality evaluation methods.