Blind binocular visual quality prediction (BVQP) is more challenging than blind monocular visual quality prediction (MVQP). Recently, the application of convolutional neural networks (CNNs) to blind MVQP has resulted in significant progress in that area. In contrast, the adoption of deep learning for blind BVQP has received scant attention. In this study, we devised an end-to-end deep fusion network (DFNet) model trained in a unified framework for blind BVQP. This core prediction engine comprises monocular feature encoding networks and binocular feature fusion networks, followed by a quality prediction layer. The monocular feature encoding networks are first established to capture the low- and high-level monocular features of the left and right retinal views, respectively. Subsequently, these monocular features are integrated by the binocular feature fusion networks to obtain binocular deep features. Finally, the final binocular visual quality is predicted by quality prediction networks. Comparisons via experiments using two standard subject-rated BVQP datasets indicate that the proposed DFNet architecture achieves highly consistent alignment with human assessment and outperforms most relevant existing models.

中文翻译：

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使用深度融合网络的盲双目视觉质量预测器


盲双眼视觉质量预测（BVQP）比盲单目视觉质量预测（MVQP）更具挑战性。最近，卷积神经网络（CNN）在盲 MVQP 中的应用在该领域取得了重大进展。相比之下，采用深度学习进行盲 BVQP 却很少受到关注。在本研究中，我们设计了一种在盲 BVQP 统一框架中训练的端到端深度融合网络 (DFNet) 模型。该核心预测引擎包括单目特征编码网络和双目特征融合网络，然后是质量预测层。首先建立单眼特征编码网络来分别捕获左、右视网膜视图的低级和高级单眼特征。随后，这些单目特征通过双目特征融合网络进行集成，以获得双目深度特征。最后，通过质量预测网络预测最终的双眼视觉质量。通过使用两个标准受试者评分 BVQP 数据集进行的实验进行比较表明，所提出的 DFNet 架构实现了与人类评估高度一致的一致性，并且优于大多数相关的现有模型。