Due to the subjective nature of image quality assessment (IQA), assessing which image has better quality among a sequence of images is more reliable than assigning an absolute mean opinion score for an image. Thus, IQA models are evaluated by global correlation consistency (GCC) metrics like PLCC and SROCC, rather than mean opinion consistency (MOC) metrics like MAE and MSE. However, most existing methods adopt MOC metrics to define their loss functions, due to the infeasible computation of GCC metrics during training. In this work, we construct a novel loss function and network to exploit Global-correlation and Mean-opinion Consistency, forming a GMC-IQA framework. Specifically, we propose a novel GCC loss by defining a pairwise preference-based rank estimation to solve the non-differentiable problem of SROCC and introducing a queue mechanism to reserve previous data to approximate the global results of the whole data. Moreover, we propose a mean-opinion network, which integrates diverse opinion features to alleviate the randomness of weight learning and enhance the model robustness. Experiments indicate that our method outperforms SOTA methods on multiple authentic datasets with higher accuracy and generalization. We also adapt the proposed loss to various networks, which brings better performance and more stable training.

翻译：由于图像质量评估（IQA）具有主观性，评估一系列图像中哪张质量更好比给单张图像分配绝对平均意见分数更可靠。因此，IQA模型通常通过全局相关性一致性（GCC）指标（如PLCC和SROCC）进行评估，而非平均意见一致性（MOC）指标（如MAE和MSE）。然而，现有方法大多采用MOC指标定义损失函数，这是因为GCC指标在训练过程中无法直接计算。本研究构建了一种新型损失函数与网络架构，以同时利用全局相关性与平均意见一致性，形成GMC-IQA框架。具体而言，我们通过定义基于成对偏好的排序估计来解决SROCC不可微问题，并引入队列机制保留历史数据以逼近全数据的全局结果，从而提出了一种新型GCC损失函数。此外，我们提出了一种平均意见网络，该网络融合多样化意见特征，以减轻权重学习的随机性并增强模型鲁棒性。实验表明，本方法在多个真实场景数据集上以更高精度和泛化能力超越现有最优方法。同时，我们将所提出的损失函数适配至多种网络架构，均能带来更优性能与更稳定的训练过程。