Understanding semantic information is an essential step in knowing what is being learned in both full-reference (FR) and no-reference (NR) image quality assessment (IQA) methods. However, especially for many severely distorted images, even if there is an undistorted image as a reference (FR-IQA), it is difficult to perceive the lost semantic and texture information of distorted images directly. In this paper, we propose a Mask Reference IQA (MR-IQA) method that masks specific patches of a distorted image and supplements missing patches with the reference image patches. In this way, our model only needs to input the reconstructed image for quality assessment. First, we design a mask generator to select the best candidate patches from reference images and supplement the lost semantic information in distorted images, thus providing more reference for quality assessment; in addition, the different masked patches imply different data augmentations, which favors model training and reduces overfitting. Second, we provide a Mask Reference Network (MRNet): the dedicated modules can prevent disturbances due to masked patches and help eliminate the patch discontinuity in the reconstructed image. Our method achieves state-of-the-art performances on the benchmark KADID-10k, LIVE and CSIQ datasets and has better generalization performance across datasets. The code and results are available in the supplementary material.

翻译：理解语义信息是全参考（FR）和无参考（NR）图像质量评估（IQA）方法中理解学习内容的关键步骤。然而，特别是对于许多严重失真的图像，即使有未失真图像作为参考（FR-IQA），直接感知失真图像丢失的语义和纹理信息也十分困难。本文提出一种掩膜参考IQA（MR-IQA）方法，该方法对失真图像的特定块进行掩膜，并用参考图像块补全缺失部分。这样，我们的模型只需输入重建图像进行质量评估。首先，我们设计一个掩膜生成器，从参考图像中选择最佳候选块，补全失真图像中丢失的语义信息，从而为质量评估提供更多参考；此外，不同的掩膜块隐含不同的数据增强方式，这有利于模型训练并减少过拟合。其次，我们提供掩膜参考网络（MRNet）：专用模块可防止掩膜块带来的干扰，并帮助消除重建图像中的块不连续性。我们的方法在KADID-10k、LIVE和CSIQ基准数据集上实现了最先进的性能，并在跨数据集上具有更好的泛化能力。代码和结果见补充材料。