TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908453955436544 |
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| author | Sureddi, Rajesh Zadtootaghaj, Saman Barman, Nabajeet Bovik, Alan C. |
| author_facet | Sureddi, Rajesh Zadtootaghaj, Saman Barman, Nabajeet Bovik, Alan C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets Sureddi, Rajesh Zadtootaghaj, Saman Barman, Nabajeet Bovik, Alan C. Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.12687 |