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:理解语义信息是了解全参考(FR)和不参考(NR)图像质量评估(IQA)方法所学内容的关键一步。然而,对于许多严重扭曲的图像,特别是许多严重扭曲的图像,即使有非扭曲的图像作为参考(FR-IQA),也很难直接看到被丢失的扭曲图像的语义和纹理信息。在本文中,我们建议使用遮罩参考 IQA(MR-IQA)(MR-IQA)(MR-IQA)方法,以遮盖被扭曲图像的具体补补补补补补遗漏的图像补补补补补。第二,我们提供遮罩参考网络(MRNet):通过这种方式,我们的模型只需输入重建图像的重建图像中的最佳候选人补补补补补,从而补充被扭曲图像中丢失的语义信息(FR-IQA),从而提供更多用于质量评估的参考资料;此外,不同的遮罩补补补补补补补补补补显示数据意味着不同的数据扩增能力,这有利于模式培训,减少过度。 其次,我们提供遮罩参考网络(MNet)网络:专用模块模块模块模块可以防止干扰,帮助消除卡-D-10-AS基图像的补补补缺数据。</s>