AI RESEARCH
Understanding Pure Textual Reasoning for Blind Image Quality Assessment
arXiv CS.CV
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ArXi:2601.02441v2 Announce Type: replace Textual reasoning has recently been widely adopted in Blind Image Quality Assessment (BIQA). However, it remains unclear how textual information contributes to quality prediction and to what extent text can represent the score-related image contents. This work addresses these questions from an information-flow perspective by comparing existing BIQA models with three paradigms designed to learn the image-text-score relationship: Chain-of-Thought, Self-Consistency, and Autoencoder.