  We introduce a Depicted image Quality Assessment method (DepictQA),
overcoming the constraints of traditional score-based methods. DepictQA allows
for detailed, language-based, human-like evaluation of image quality by
leveraging Multi-modal Large Language Models (MLLMs). Unlike conventional Image
Quality Assessment (IQA) methods relying on scores, DepictQA interprets image
content and distortions descriptively and comparatively, aligning closely with
humans' reasoning process. To build the DepictQA model, we establish a
hierarchical task framework, and collect a multi-modal IQA training dataset. To
tackle the challenges of limited training data and multi-image processing, we
propose to use multi-source training data and specialized image tags. These
designs result in a better performance of DepictQA than score-based approaches
on multiple benchmarks. Moreover, compared with general MLLMs, DepictQA can
generate more accurate reasoning descriptive languages. We also demonstrate
that our full-reference dataset can be extended to non-reference applications.
These results showcase the research potential of multi-modal IQA methods. Codes
and datasets are available in https://depictqa.github.io.
