标题： TRIQA：通过在有序失真三元组上对比预训练进行图像质量评估 显示英文标题

标题： TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets

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。 无参考（NR）IQA由于缺乏参考图像而特别具有挑战性。 虽然深度学习在这一领域取得了显著进展，但开发NR-IQA模型的主要障碍是主观标记数据的可用性有限。 大多数现有的基于深度学习的NR-IQA方法在进行IQA任务微调之前依赖于在大规模数据集上进行预训练。 为了进一步推动该领域的发展，我们提出了一种新方法，该方法使用少量参考内容图像构建自定义数据集，并引入了一个结合内容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训练一个质量感知模型，这使得在较少样本的情况下也能实现高效的训练，并在公开可用的数据集上实现了强大的泛化性能。 我们的代码库可在 https://github.com/rajeshsureddi/triqa 获取。 显示英文摘要

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.