Interpretable Image Quality Assessment via CLIP with Multiple Antonym-Prompt Pairs

TL;DR

This paper introduces a zero-shot, interpretable image quality assessment method using CLIP with antonym prompts, enabling both quality evaluation and cause identification without task-specific training.

Contribution

It proposes a novel prompt pairing strategy with antonym prompts for zero-shot NR-IQA, enhancing interpretability and accuracy over existing methods.

Findings

Outperforms existing zero-shot NR-IQA methods in accuracy

Can identify causes of perceptual quality degradation

Demonstrates effectiveness without task-specific training

Abstract

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.