  Image Quality Assessment (IQA) models benefit significantly from semantic
information, which allows them to treat different types of objects distinctly.
Currently, leveraging semantic information to enhance IQA is a crucial research
direction. Traditional methods, hindered by a lack of sufficiently annotated
data, have employed the CLIP image-text pretraining model as their backbone to
gain semantic awareness. However, the generalist nature of these pre-trained
Vision-Language (VL) models often renders them suboptimal for IQA-specific
tasks. Recent approaches have attempted to address this mismatch using prompt
technology, but these solutions have shortcomings. Existing prompt-based VL
models overly focus on incremental semantic information from text, neglecting
the rich insights available from visual data analysis. This imbalance limits
their performance improvements in IQA tasks. This paper introduces an
innovative multi-modal prompt-based methodology for IQA. Our approach employs
carefully crafted prompts that synergistically mine incremental semantic
information from both visual and linguistic data. Specifically, in the visual
branch, we introduce a multi-layer prompt structure to enhance the VL model's
adaptability. In the text branch, we deploy a dual-prompt scheme that steers
the model to recognize and differentiate between scene category and distortion
type, thereby refining the model's capacity to assess image quality. Our
experimental findings underscore the effectiveness of our method over existing
Blind Image Quality Assessment (BIQA) approaches. Notably, it demonstrates
competitive performance across various datasets. Our method achieves Spearman
Rank Correlation Coefficient (SRCC) values of 0.961(surpassing 0.946 in CSIQ)
and 0.941 (exceeding 0.930 in KADID), illustrating its robustness and accuracy
in diverse contexts.
