轻量级模型实现高清图像质量评估，适合移动端实时使用。
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation
- 用轻量化网络和高分辨率输入保留细节，避免图像裁剪损失。
- 通过多视角注意力捕捉不同评价者观点，提升评估主观一致性。
- 知识蒸馏使模型更小更快，兼顾精度与效率，适合手机端部署。
随着对高分辨率（HR）图像需求的增长，无参考图像质量评估（NR-IQA）受到更多关注，因其可在移动设备上实时评估图像质量，提升用户体验。然而，现有方法通常将高分辨率图像缩放或裁剪为低分辨率，导致重要细节丢失；且多数模型计算复杂度高，在移动设备有限算力下难以应用。为此，我们提出 MobileIQA，采用轻量级骨干网络，在保留高分辨率输入的前提下高效评估图像质量。MobileIQA 引入多视角注意力学习（MAL）模块，模拟数据标注过程中不同标注者提供的主观意见。模型通过教师-学生知识蒸馏机制，显著降低计算复杂度同时保持高性能。实验表明，MobileIQA 在多个评估指标和计算效率上均优于现有新型 IQA 方法。代码已开源：https://github.com/chencn2020/MobileIQA。
原文摘要 · Abstract (English)
With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience. However, existing NR-IQA methods often resize or crop the HR images into small resolution, which leads to a loss of important details. And most of them are of high computational complexity, which hinders their application on mobile devices due to limited computational resources. To address these challenges, we propose MobileIQA, a novel approach that utilizes lightweight backbones to efficiently assess image quality while preserving image details through high-resolution input. MobileIQA employs the proposed multi-view attention learning (MAL) module to capture diverse opinions, simulating subjective opinions provided by different annotators during the dataset annotation process. The model uses a teacher model to guide the learning of a student model through knowledge distillation. This method significantly reduces computational complexity while maintaining high performance. Experiments demonstrate that MobileIQA outperforms novel IQA methods on evaluation metrics and computational efficiency. The code is available at https://github.com/chencn2020/MobileIQA.
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