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.
@article{arxiv.2507.12687,
title = {TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets},
author = {Rajesh Sureddi and Saman Zadtootaghaj and Nabajeet Barman and Alan C. Bovik},
journal= {arXiv preprint arXiv:2507.12687},
year = {2025}
}