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

• URL: http://arxiv.org/abs/2507.12687v1
• Date: Wed, 16 Jul 2025 23:43:12 GMT
• Title: TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets
• Authors: Rajesh Sureddi, Saman Zadtootaghaj, Nabajeet Barman, Alan C. Bovik,
• Abstract summary: No-Reference (NR) IQA remains particularly challenging due to the absence of a reference image.<n>We propose a novel approach that constructs a custom dataset using a limited number of reference content images.<n>We train a quality-aware model using contrastive triplet-based learning, enabling efficient training with fewer samples.
• Score: 31.2422359004089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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