  Recent advances of large multi-modality models (LMM) have greatly improved
the ability of image quality assessment (IQA) method to evaluate and explain
the quality of visual content. However, these advancements are mostly focused
on overall quality assessment, and the detailed examination of local quality,
which is crucial for comprehensive visual understanding, is still largely
unexplored. In this work, we introduce Q-Ground, the first framework aimed at
tackling fine-scale visual quality grounding by combining large multi-modality
models with detailed visual quality analysis. Central to our contribution is
the introduction of the QGround-100K dataset, a novel resource containing 100k
triplets of (image, quality text, distortion segmentation) to facilitate deep
investigations into visual quality. The dataset comprises two parts: one with
human-labeled annotations for accurate quality assessment, and another labeled
automatically by LMMs such as GPT4V, which helps improve the robustness of
model training while also reducing the costs of data collection. With the
QGround-100K dataset, we propose a LMM-based method equipped with multi-scale
feature learning to learn models capable of performing both image quality
answering and distortion segmentation based on text prompts. This
dual-capability approach not only refines the model's understanding of
region-aware image quality but also enables it to interactively respond to
complex, text-based queries about image quality and specific distortions.
Q-Ground takes a step towards sophisticated visual quality analysis in a finer
scale, establishing a new benchmark for future research in the area. Codes and
dataset are available at https://github.com/Q-Future/Q-Ground.
