Quality Assessment in the Era of Large Models: A Survey
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910585876119552 |
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| author | Zhang, Zicheng Zhou, Yingjie Li, Chunyi Zhao, Baixuan Liu, Xiaohong Zhai, Guangtao |
| author_facet | Zhang, Zicheng Zhou, Yingjie Li, Chunyi Zhao, Baixuan Liu, Xiaohong Zhai, Guangtao |
| contents | Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts. Before the advent of large models, quality assessment typically relied on small expert models tailored for specific tasks. While these smaller models are effective at handling their designated tasks and predicting quality levels, they often lack explainability and robustness. With the advancement of large models, which align more closely with human cognitive and perceptual processes, many researchers are now leveraging the prior knowledge embedded in these large models for quality assessment tasks. This emergence of quality assessment within the context of large models motivates us to provide a comprehensive review focusing on two key aspects: 1) the assessment of large models, and 2) the role of large models in assessment tasks. We begin by reflecting on the historical development of quality assessment. Subsequently, we move to detailed discussions of related works concerning quality assessment in the era of large models. Finally, we offer insights into the future progression and potential pathways for quality assessment in this new era. We hope this survey will enable a rapid understanding of the development of quality assessment in the era of large models and inspire further advancements in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00031 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quality Assessment in the Era of Large Models: A Survey Zhang, Zicheng Zhou, Yingjie Li, Chunyi Zhao, Baixuan Liu, Xiaohong Zhai, Guangtao Human-Computer Interaction Artificial Intelligence Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts. Before the advent of large models, quality assessment typically relied on small expert models tailored for specific tasks. While these smaller models are effective at handling their designated tasks and predicting quality levels, they often lack explainability and robustness. With the advancement of large models, which align more closely with human cognitive and perceptual processes, many researchers are now leveraging the prior knowledge embedded in these large models for quality assessment tasks. This emergence of quality assessment within the context of large models motivates us to provide a comprehensive review focusing on two key aspects: 1) the assessment of large models, and 2) the role of large models in assessment tasks. We begin by reflecting on the historical development of quality assessment. Subsequently, we move to detailed discussions of related works concerning quality assessment in the era of large models. Finally, we offer insights into the future progression and potential pathways for quality assessment in this new era. We hope this survey will enable a rapid understanding of the development of quality assessment in the era of large models and inspire further advancements in the field. |
| title | Quality Assessment in the Era of Large Models: A Survey |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2409.00031 |