Comparative Evaluation of Multimodal Large Language Models for No-Reference Image Quality Assessment with Authentic Distortions: A Study of OpenAI and Claude.AI Models
This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI and three models from Claude.AI, comparing their p...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/5/132 |
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| Summary: | This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI and three models from Claude.AI, comparing their performance in estimating image quality without reference images. Our results demonstrate that these LLMs outperform traditional methods based on hand-crafted features. However, more advanced deep learning models, especially those based on deep convolutional networks, surpass LLMs in performance. Notably, we make a unique contribution by publishing the processed outputs of the LLMs, providing a transparent and direct comparison of their quality assessments based solely on the predicted quality scores. This work underscores the potential of multimodal LLMs in image quality evaluation, while also highlighting the continuing advantages of specialized deep learning approaches. |
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| ISSN: | 2504-2289 |