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Assessing Portrait Quality in Digital Photography: Methods, Challenges, and Innovations| title | Assessing Portrait Quality in Digital Photography: Methods, Challenges, and Innovations |
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| start_date | 2024/02/06 |
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| schedule | 15h |
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| online | no |
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| location_info | Salle 314 |
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| summary | This talk focuses on the development of deep learning-based image quality assessment methods tailored for digital portrait photography. Emphasizing the estimation of portrait-specific quality attributes, it addresses the challenges in predicting various global and local aspects such as color balance, detail rendering, and facial features across a variety of scenarios. Additionally, the seminar introduces PIQ23, a comprehensive portrait-specific IQA dataset. This dataset includes images from a wide range of smartphone models, annotated for key quality attributes by expert evaluators. The discussion will highlight the dataset's role in understanding the consistency of quality assessments and the potential of integrating semantic information to improve IQA predictions in portrait photography. |
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| responsibles | Vacher, Blusseau |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2024/02/05 13:31 UTC |
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