Song Tianshu, Li Leida, Zhu Hancheng, Qian Jiansheng
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
School of Artificial Intelligence, Xidian University, Xi'an, China.
Front Neurosci. 2021 Oct 21;15:739138. doi: 10.3389/fnins.2021.739138. eCollection 2021.
Image quality assessment (IQA) for authentic distortions in the wild is challenging. Though current IQA metrics have achieved decent performance for synthetic distortions, they still cannot be satisfactorily applied to realistic distortions because of the generalization problem. Improving generalization ability is an urgent task to make IQA algorithms serviceable in real-world applications, while relevant research is still rare. Fundamentally, image quality is determined by both distortion degree and intelligibility. However, current IQA metrics mostly focus on the distortion aspect and do not fully investigate the intelligibility, which is crucial for achieving robust quality estimation. Motivated by this, this paper presents a new framework for building highly generalizable image quality model by integrating the intelligibility. We first analyze the relation between intelligibility and image quality. Then we propose a bilateral network to integrate the above two aspects of image quality. During the fusion process, feature selection strategy is further devised to avoid negative transfer. The framework not only catches the conventional distortion features but also integrates intelligibility features properly, based on which a highly generalizable no-reference image quality model is achieved. Extensive experiments are conducted based on five intelligibility tasks, and the results demonstrate that the proposed approach outperforms the state-of-the-art metrics, and the intelligibility task consistently improves metric performance and generalization ability.
针对真实场景中失真的图像质量评估（IQA）具有挑战性。尽管当前的IQA指标在合成失真方面取得了不错的性能，但由于泛化问题，它们仍然不能令人满意地应用于实际失真情况。提高泛化能力是使IQA算法在实际应用中可用的紧迫任务，而相关研究仍然很少。从根本上说，图像质量由失真程度和可懂度共同决定。然而，当前的IQA指标大多关注失真方面，没有充分研究可懂度，而可懂度对于实现稳健的质量估计至关重要。受此启发，本文提出了一个通过整合可懂度来构建高度可泛化图像质量模型的新框架。我们首先分析可懂度与图像质量之间的关系。然后我们提出一个双边网络来整合图像质量的上述两个方面。在融合过程中，进一步设计了特征选择策略以避免负迁移。该框架不仅捕捉了传统的失真特征，还适当地整合了可懂度特征，在此基础上实现了一个高度可泛化的无参考图像质量模型。基于五个可懂度任务进行了广泛的实验，结果表明所提出的方法优于当前最先进的指标，并且可懂度任务持续提高了指标性能和泛化能力。
