Evaluating Vision-Language Models for Image Quality Assessment using Psychophysical Data
Psychophysical experiments remain the most reliable approach for perceptual image quality assessment (IQA), yet their cost and limited scalability motivate automated alternatives. This paper investigates whether Vision-Language Models (VLMs) can assist in assessing perceived image appearance and quality. We introduce a psychophysics-inspired framework to probe VLM perceptual sensitivity through controlled pairwise image comparisons of contrast, colorfulness, and overall preference. Six VLMs (four proprietary and two open-weight models) are compared against psychophysical data. Results reveal strong attribute-dependent variability: Claude exhibits the highest internal consistency, whereas ...
| Authors | Imran Mehmood, Imad Ali Shah, Ming Ronnier Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 March 2026 |
| Last updated at source | 5 September 2026 |
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