2AFC Prompting of Large Multimodal Models for Image Quality Assessment
This work addresses the need for reliable image quality assessment in AI systems, but it is incremental as it adapts an existing method (2AFC) to a new application area with LMMs.
The paper tackled the under-explored problem of visual quality assessment (IQA) in large multimodal models (LMMs) by using two-alternative forced choice (2AFC) prompting and maximum a posterior estimation, finding that existing LMMs show remarkable ability in coarse-grained quality comparison but need improvement in fine-grained discrimination.
While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs.