Interpretable Image Quality Assessment via CLIP with Multiple
  Antonym-Prompt Pairs
Takamichi Miyata
No reference image quality assessment (NR-IQA) is a task to estimate the
perceptual quality of an image without its corresponding original image. It is
even more difficult to perform this task in a zero-shot manner, i.e., without
task-specific training. In this paper, we propose a new zero-shot and
interpretable NRIQA method that exploits the ability of a pre-trained
visionlanguage model to estimate the correlation between an image and a textual
prompt. The proposed method employs a prompt pairing strategy and multiple
antonym-prompt pairs corresponding to carefully selected descriptive features
corresponding to the perceptual image quality. Thus, the proposed method is
able to identify not only the perceptual quality evaluation of the image, but
also the cause on which the quality evaluation is based. Experimental results
show that the proposed method outperforms existing zero-shot NR-IQA methods in
terms of accuracy and can evaluate the causes of perceptual quality
degradation.