Interpretable Image Quality Assessment via CLIP with Multiple Antonym-Prompt Pairs
This work addresses the challenge of interpretable image quality assessment for applications like media processing, though it is incremental as it builds on existing vision-language models.
The paper tackles the problem of no-reference image quality assessment (NR-IQA) in a zero-shot manner by proposing a method that uses a pre-trained vision-language model with multiple antonym-prompt pairs to estimate perceptual quality and identify degradation causes, achieving improved accuracy over existing zero-shot methods.
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.