基于人眼视觉特性的深度学习全参考图像质量评价方法

针对现有的图像质量评价方法普遍为人工设计特征，难以自动且有效提取到符合人类视觉系统的图像特征，受人眼视觉特性的启发，提出一种新的基于卷积神经网络的全参考图像质量评价方法（DeepFR）。该方法基于对数据集本身的学习设计了卷积神经网络DeepFR模型，利用人眼视觉系统对梯度的敏感性进行加权优化，提取了符合人眼视觉特性的视觉感知图。实验表明:设计的DeepFR模型优于已有的全参考图像质量评价方法，其预测结果与主观质量评价有很好的精确性与一致性。

Deep learning of full-reference image quality assessment based on human visual properties

Since the current image quality assessment methods are generally based on hand-crafted features, it is difficult to automatically and effectively extract image features that conform to the human visual system. Inspired by human visual characteristics, a new method of full-reference image quality assessment was proposed by this paper which was based on convolutional neural network (DeepFR). According to this method, the DeepFR model of convolutional neural network was designed which was based on the understanding of the dataset by itself using the human visual system to weight the sensitivity of the gradient, and the visual gradient perception map was extracted that was consistent with human visual characteristics. The experimental results show that the DeepFR model is superior to the current full-reference image quality assessment methods, its prediction score and subjective quality evaluation have good accuracy and consistency.