DEFNet：基于多任务的深度证据融合网络用于盲图像质量评估
DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment
AI总结：
DEFNet通过多任务优化和证据学习改进盲图像质量评估，提升鲁棒性和泛化能力。
摘要：
盲图像质量评估（BIQA）方法通常会结合辅助任务来提高性能。然而，现有方法由于整合不足和缺乏灵活的不确定性估计，导致性能不理想。为解决这些问题，我们提出了一种基于多任务的深度证据融合网络（DEFNet）用于BIQA，该网络通过场景和失真类型分类任务的帮助进行多任务优化。为了实现更稳健和可靠的表示，我们设计了一种新颖的可信信息融合策略。它首先结合子区域中的多样化特征和模式以增强信息丰富性，然后通过平衡细粒度细节与粗粒度上下文进行局部-全局信息融合。此外，DEFNet利用受证据学习启发的先进不确定性估计技术，通过正态-逆伽玛分布混合来实现。在合成和真实失真数据集上的大量实验验证了所提框架的有效性和鲁棒性。此外，进一步的评估和分析用于突出其强大的泛化能力和对以前未见场景的适应性。
英文摘要：
Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integration and a lack of flexible uncertainty estimation, leading to suboptimal performance. To address these challenges, we propose a multitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which performs multitask optimization with the assistance of scene and distortion type classification tasks. To achieve a more robust and reliable representation, we design a novel trustworthy information fusion strategy. It first combines diverse features and patterns across sub-regions to enhance information richness, and then performs local-global information fusion by balancing fine-grained details with coarse-grained context. Moreover, DEFNet exploits advanced uncertainty estimation technique inspired by evidential learning with the help of normal-inverse gamma distribution mixture. Extensive experiments on both synthetic and authentic distortion datasets demonstrate the effectiveness and robustness of the proposed framework. Additional evaluation and analysis are carried out to highlight its strong generalization capability and adaptability to previously unseen scenarios.
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