  With the rapid advancement of Vision Language Models (VLMs), VLM-based Image
Quality Assessment (IQA) seeks to describe image quality linguistically to
align with human expression and capture the multifaceted nature of IQA tasks.
However, current methods are still far from practical usage. First, prior works
focus narrowly on specific sub-tasks or settings, which do not align with
diverse real-world applications. Second, their performance is sub-optimal due
to limitations in dataset coverage, scale, and quality. To overcome these
challenges, we introduce Depicted image Quality Assessment in the Wild
(DepictQA-Wild). Our method includes a multi-functional IQA task paradigm that
encompasses both assessment and comparison tasks, brief and detailed responses,
full-reference and non-reference scenarios. We introduce a
ground-truth-informed dataset construction approach to enhance data quality,
and scale up the dataset to 495K under the brief-detail joint framework.
Consequently, we construct a comprehensive, large-scale, and high-quality
dataset, named DQ-495K. We also retain image resolution during training to
better handle resolution-related quality issues, and estimate a confidence
score that is helpful to filter out low-quality responses. Experimental results
demonstrate that DepictQA-Wild significantly outperforms traditional
score-based methods, prior VLM-based IQA models, and proprietary GPT-4V in
distortion identification, instant rating, and reasoning tasks. Our advantages
are further confirmed by real-world applications including assessing the
web-downloaded images and ranking model-processed images. Datasets and codes
will be released in https://depictqa.github.io/depictqa-wild/.
