Understanding Pure Textual Reasoning for Blind Image Quality Assessment

1. 1Analyze existing BIQA models to understand their limitations
2. 2Design new paradigms to learn the image-text-score relationship
3. 3Compare the performance of existing models with the new paradigms
4. 4Evaluate the contribution of textual information to quality prediction

Machine learning researchers and engineers working on computer vision tasks, particularly those focused on image quality assessment, can benefit from understanding the role of textual reasoning in this context. This knowledge can help them design more effective models for blind image quality assessment

💡 Textual information can effectively represent score-related image contents and improve quality prediction

📸💡 Textual reasoning boosts blind image quality assessment #BIQA #ComputerVision