Abstract: To improve performance of current Non-Reference Image Quality Assessment (NR-IQA) methods, a no-reference image quality assessment algorithm with enhanced adversarial learning was proposed under the latest deep Generative Adversarial Network (GAN) technology. In the proposed algorithm, the adversarial learning was strengthened by improving the loss function and the structure of the network model, so as to output more reliable simulated "reference images" to simulate human visual comparison process as the Full-Reference Image Quality Assessment (FR-IQA) method does. First, the distorted image and undistorted original image were input to train the network model based on the enhanced adversarial learning. Then, a simulated image of the image to be tested was output from the trained model, and the deep convolution features of the reference image were extracted. Finally, the deep convolution features of reference image and the distorted image to be tested were merged and input into the trained quality assessment regression network, and the assessment score of the image was output. Datasets LIVE, TID2008 and TID2013 were used to perform the experiments. Experimental results show that the overall subjective performance on image quality assessment of the proposed algorithm is superior to those of the existing mainstream algorithms and is consistent with the performance of the human subjective assessment.

Key words: Non-Reference Image Quality Assessment (NR-IQA), Generative Adversarial Network (GAN), deep learning, generative model, discriminative model

摘要： 为了提高无参考图像质量评价（NR-IQA）方法的性能，参考先进的深度生成对抗网络（GAN）研究成果，提出一种基于增强型对抗学习的无参考图像质量评价算法，即通过改进损失函数、网络模型结构来增强对抗学习强度，输出更可靠的模拟“参考图”，进而可以像全参考图像质量评价（FR-IQA）方法一样模拟人的视觉比较过程。首先，利用数据集中失真的图像和未失真的原图像作为输入，从而基于增强对抗学习来训练网络模型；然后，利用该模型输出待测图像的模拟仿真图，提取仿真图的深度卷积特征；最后，将仿真图和待测失真图的卷积特征相融合，并输入到训练好的图像质量评价回归网络，输出图像的评测分数。在LIVE、TID2008和TID2013数据集上完成实验。实验结果表明，所提算法在图像质量上的总体客观评价性能优于当前的主流算法，与人的主观评价表现出的性能相一致。

关键词: 无参考图像质量评价, 生成对抗网络, 深度学习, 生成模型, 判别模型