  In this work, we take the first exploration of the recently popular
foundation model, i.e., State Space Model/Mamba, in image quality assessment,
aiming at observing and excavating the perception potential in vision Mamba. A
series of works on Mamba has shown its significant potential in various fields,
e.g., segmentation and classification. However, the perception capability of
Mamba has been under-explored. Consequently, we propose Q-Mamba by revisiting
and adapting the Mamba model for three crucial IQA tasks, i.e., task-specific,
universal, and transferable IQA, which reveals that the Mamba model has obvious
advantages compared with existing foundational models, e.g., Swin Transformer,
ViT, and CNNs, in terms of perception and computational cost for IQA. To
increase the transferability of Q-Mamba, we propose the StylePrompt tuning
paradigm, where the basic lightweight mean and variance prompts are injected to
assist the task-adaptive transfer learning of pre-trained Q-Mamba for different
downstream IQA tasks. Compared with existing prompt tuning strategies, our
proposed StylePrompt enables better perception transfer capability with less
computational cost. Extensive experiments on multiple synthetic, authentic IQA
datasets, and cross IQA datasets have demonstrated the effectiveness of our
proposed Q-Mamba.
