TRIQA：基于有序失真三元组对比预训练的图像质量评估
TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets
AI总结：
本文提出TRIQA方法，通过有限参考图像构建有序失真三元组数据集，并以对比学习训练融合内容与质量特征的无参考IQA模型，实现少样本高效训练和强泛化性能。
摘要：
图像质量评估（IQA）模型旨在预测与人类判断一致的感知图像质量。由于缺少参考图像，无参考（NR）IQA尤其具有挑战性。尽管深度学习显著推动了该领域的发展，但开发NR-IQA模型的一个主要障碍是主观标注数据的有限可用性。现有的大多数基于深度学习的NR-IQA方法依赖于在大规模数据集上进行预训练，然后再针对IQA任务进行微调。为了进一步推动该领域的进展，我们提出了一种新方法：利用有限数量的参考内容图像构建自定义数据集，并引入一种同时融合内容特征和质量特征的无参考IQA模型，用于感知质量预测。具体而言，我们使用基于对比三元组的学习来训练一个质量感知模型，从而能够以更少的样本实现高效训练，同时在公开数据集上取得强大的泛化性能。我们的代码仓库可在https://github.com/rajeshsureddi/triqa获取。
英文摘要：
Image Quality Assessment (IQA) models aim to predict perceptual image quality in alignment with human judgments. No-Reference (NR) IQA remains particularly challenging due to the absence of a reference image. While deep learning has significantly advanced this field, a major hurdle in developing NR-IQA models is the limited availability of subjectively labeled data. Most existing deep learning-based NR-IQA approaches rely on pre-training on large-scale datasets before fine-tuning for IQA tasks. To further advance progress in this area, we propose a novel approach that constructs a custom dataset using a limited number of reference content images and introduces a no-reference IQA model that incorporates both content and quality features for perceptual quality prediction. Specifically, we train a quality-aware model using contrastive triplet-based learning, enabling efficient training with fewer samples while achieving strong generalization performance across publicly available datasets. Our repository is available at https://github.com/rajeshsureddi/triqa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
